chore: release v4.2.9.dev10

stupid events

.github/workflows/build-container.yml

@@ -13,12 +13,6 @@ on:
     tags:
       - 'v*.*.*'
   workflow_dispatch:
-    inputs:
-      push-to-registry:
-        description: Push the built image to the container registry
-        required: false
-        type: boolean
-        default: false
 
 permissions:
   contents: write
@@ -56,15 +50,16 @@ jobs:
           df -h
 
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Docker meta
         id: meta
-        uses: docker/metadata-action@v5
+        uses: docker/metadata-action@v4
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
           images: |
             ghcr.io/${{ github.repository }}
+            ${{ env.DOCKERHUB_REPOSITORY }}
           tags: |
             type=ref,event=branch
             type=ref,event=tag
@@ -77,33 +72,49 @@ jobs:
             suffix=-${{ matrix.gpu-driver }},onlatest=false
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
+        uses: docker/setup-qemu-action@v2
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
         with:
           platforms: ${{ env.PLATFORMS }}
 
       - name: Login to GitHub Container Registry
         if: github.event_name != 'pull_request'
-        uses: docker/login-action@v3
+        uses: docker/login-action@v2
         with:
           registry: ghcr.io
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
+      # - name: Login to Docker Hub
+      #   if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
+      #   uses: docker/login-action@v2
+      #   with:
+      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
+      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
+
       - name: Build container
         timeout-minutes: 40
         id: docker_build
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v4
         with:
           context: .
           file: docker/Dockerfile
           platforms: ${{ env.PLATFORMS }}
-          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' || github.event.inputs.push-to-registry }}
+          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
           cache-from: |
             type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
             type=gha,scope=main-${{ matrix.gpu-driver }}
           cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
+
+      # - name: Docker Hub Description
+      #   if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
+      #   uses: peter-evans/dockerhub-description@v3
+      #   with:
+      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
+      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
+      #     repository: ${{ vars.DOCKERHUB_REPOSITORY }}
+      #     short-description: ${{ github.event.repository.description }}

docs/help/FAQ.md

@@ -196,22 +196,6 @@ tips to reduce the problem:
     === "12GB VRAM GPU"
 
         This should be sufficient to generate larger images up to about 1280x1280.
-		
-## Checkpoint Models Load Slowly or Use Too Much RAM
-
-The difference between diffusers models (a folder containing multiple
-subfolders) and checkpoint models (a file ending with .safetensors or
-.ckpt) is that InvokeAI is able to load diffusers models into memory
-incrementally, while checkpoint models must be loaded all at
-once. With very large models, or systems with limited RAM, you may
-experience slowdowns and other memory-related issues when loading
-checkpoint models.
-
-To solve this, go to the Model Manager tab (the cube), select the
-checkpoint model that's giving you trouble, and press the "Convert"
-button in the upper right of your browser window. This will conver the
-checkpoint into a diffusers model, after which loading should be
-faster and less memory-intensive.
 
 ## Memory Leak (Linux)
 

invokeai/app/api/routers/model_manager.py

@@ -3,10 +3,8 @@
 
 import io
 import pathlib
-import shutil
 import traceback
 from copy import deepcopy
-from enum import Enum
 from tempfile import TemporaryDirectory
 from typing import List, Optional, Type
 
@@ -19,7 +17,6 @@ from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.config import get_config
 from invokeai.app.services.model_images.model_images_common import ModelImageFileNotFoundException
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob
 from invokeai.app.services.model_records import (
@@ -34,7 +31,6 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch
@@ -54,13 +50,6 @@ class ModelsList(BaseModel):
     model_config = ConfigDict(use_enum_values=True)
 
 
-class CacheType(str, Enum):
-    """Cache type - one of vram or ram."""
-
-    RAM = "RAM"
-    VRAM = "VRAM"
-
-
 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)
@@ -808,83 +797,3 @@ async def get_starter_models() -> list[StarterModel]:
         model.dependencies = missing_deps
 
     return starter_models
-
-
-@model_manager_router.get(
-    "/model_cache",
-    operation_id="get_cache_size",
-    response_model=float,
-    summary="Get maximum size of model manager RAM or VRAM cache.",
-)
-async def get_cache_size(cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM)) -> float:
-    """Return the current RAM or VRAM cache size setting (in GB)."""
-    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
-    value = 0.0
-    if cache_type == CacheType.RAM:
-        value = cache.max_cache_size
-    elif cache_type == CacheType.VRAM:
-        value = cache.max_vram_cache_size
-    return value
-
-
-@model_manager_router.put(
-    "/model_cache",
-    operation_id="set_cache_size",
-    response_model=float,
-    summary="Set maximum size of model manager RAM or VRAM cache, optionally writing new value out to invokeai.yaml config file.",
-)
-async def set_cache_size(
-    value: float = Query(description="The new value for the maximum cache size"),
-    cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM),
-    persist: bool = Query(description="Write new value out to invokeai.yaml", default=False),
-) -> float:
-    """Set the current RAM or VRAM cache size setting (in GB). ."""
-    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
-    app_config = get_config()
-    # Record initial state.
-    vram_old = app_config.vram
-    ram_old = app_config.ram
-
-    # Prepare target state.
-    vram_new = vram_old
-    ram_new = ram_old
-    if cache_type == CacheType.RAM:
-        ram_new = value
-    elif cache_type == CacheType.VRAM:
-        vram_new = value
-    else:
-        raise ValueError(f"Unexpected {cache_type=}.")
-
-    config_path = app_config.config_file_path
-    new_config_path = config_path.with_suffix(".yaml.new")
-
-    try:
-        # Try to apply the target state.
-        cache.max_vram_cache_size = vram_new
-        cache.max_cache_size = ram_new
-        app_config.ram = ram_new
-        app_config.vram = vram_new
-        if persist:
-            app_config.write_file(new_config_path)
-            shutil.move(new_config_path, config_path)
-    except Exception as e:
-        # If there was a failure, restore the initial state.
-        cache.max_cache_size = ram_old
-        cache.max_vram_cache_size = vram_old
-        app_config.ram = ram_old
-        app_config.vram = vram_old
-
-        raise RuntimeError("Failed to update cache size") from e
-    return value
-
-
-@model_manager_router.get(
-    "/stats",
-    operation_id="get_stats",
-    response_model=Optional[CacheStats],
-    summary="Get model manager RAM cache performance statistics.",
-)
-async def get_stats() -> Optional[CacheStats]:
-    """Return performance statistics on the model manager's RAM cache. Will return null if no models have been loaded."""
-
-    return ApiDependencies.invoker.services.model_manager.load.ram_cache.stats

invokeai/app/api/routers/session_queue.py

@@ -11,7 +11,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByDestinationResult,
+    CancelByOriginResult,
     ClearResult,
     EnqueueBatchResult,
     PruneResult,
@@ -107,18 +107,16 @@ async def cancel_by_batch_ids(
 
 
 @session_queue_router.put(
-    "/{queue_id}/cancel_by_destination",
-    operation_id="cancel_by_destination",
+    "/{queue_id}/cancel_by_origin",
+    operation_id="cancel_by_origin",
     responses={200: {"model": CancelByBatchIDsResult}},
 )
-async def cancel_by_destination(
+async def cancel_by_origin(
     queue_id: str = Path(description="The queue id to perform this operation on"),
-    destination: str = Query(description="The destination to cancel all queue items for"),
-) -> CancelByDestinationResult:
+    origin: str = Query(description="The origin to cancel all queue items for"),
+) -> CancelByOriginResult:
     """Immediately cancels all queue items with the given origin"""
-    return ApiDependencies.invoker.services.session_queue.cancel_by_destination(
-        queue_id=queue_id, destination=destination
-    )
+    return ApiDependencies.invoker.services.session_queue.cancel_by_origin(queue_id=queue_id, origin=origin)
 
 
 @session_queue_router.put(

invokeai/app/invocations/denoise_latents.py

@@ -185,7 +185,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     )
     denoise_mask: Optional[DenoiseMaskField] = InputField(
         default=None,
-        description=FieldDescriptions.denoise_mask,
+        description=FieldDescriptions.mask,
         input=Input.Connection,
         ui_order=8,
     )

invokeai/app/invocations/fields.py

@@ -181,7 +181,7 @@ class FieldDescriptions:
     )
     num_1 = "The first number"
     num_2 = "The second number"
-    denoise_mask = "A mask of the region to apply the denoising process to."
+    mask = "The mask to use for the operation"
     board = "The board to save the image to"
     image = "The image to process"
     tile_size = "Tile size"

invokeai/app/invocations/flux_denoise.py

@@ -1,249 +0,0 @@
-from typing import Callable, Optional
-
-import torch
-import torchvision.transforms as tv_transforms
-from torchvision.transforms.functional import resize as tv_resize
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import (
-    DenoiseMaskField,
-    FieldDescriptions,
-    FluxConditioningField,
-    Input,
-    InputField,
-    LatentsField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import TransformerField
-from invokeai.app.invocations.primitives import LatentsOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.denoise import denoise
-from invokeai.backend.flux.inpaint_extension import InpaintExtension
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.sampling_utils import (
-    clip_timestep_schedule,
-    generate_img_ids,
-    get_noise,
-    get_schedule,
-    pack,
-    unpack,
-)
-from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
-from invokeai.backend.util.devices import TorchDevice
-
-
-@invocation(
-    "flux_denoise",
-    title="FLUX Denoise",
-    tags=["image", "flux"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Run denoising process with a FLUX transformer model."""
-
-    # If latents is provided, this means we are doing image-to-image.
-    latents: Optional[LatentsField] = InputField(
-        default=None,
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    # denoise_mask is used for image-to-image inpainting. Only the masked region is modified.
-    denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None,
-        description=FieldDescriptions.denoise_mask,
-        input=Input.Connection,
-    )
-    denoising_start: float = InputField(
-        default=0.0,
-        ge=0,
-        le=1,
-        description=FieldDescriptions.denoising_start,
-    )
-    denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
-    transformer: TransformerField = InputField(
-        description=FieldDescriptions.flux_model,
-        input=Input.Connection,
-        title="Transformer",
-    )
-    positive_text_conditioning: FluxConditioningField = InputField(
-        description=FieldDescriptions.positive_cond, input=Input.Connection
-    )
-    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
-    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
-    num_steps: int = InputField(
-        default=4, description="Number of diffusion steps. Recommended values are schnell: 4, dev: 50."
-    )
-    guidance: float = InputField(
-        default=4.0,
-        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
-    )
-    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = self._run_diffusion(context)
-        latents = latents.detach().to("cpu")
-
-        name = context.tensors.save(tensor=latents)
-        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
-
-    def _run_diffusion(
-        self,
-        context: InvocationContext,
-    ):
-        inference_dtype = torch.bfloat16
-
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        flux_conditioning = cond_data.conditionings[0]
-        assert isinstance(flux_conditioning, FLUXConditioningInfo)
-        flux_conditioning = flux_conditioning.to(dtype=inference_dtype)
-        t5_embeddings = flux_conditioning.t5_embeds
-        clip_embeddings = flux_conditioning.clip_embeds
-
-        # Load the input latents, if provided.
-        init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
-        if init_latents is not None:
-            init_latents = init_latents.to(device=TorchDevice.choose_torch_device(), dtype=inference_dtype)
-
-        # Prepare input noise.
-        noise = get_noise(
-            num_samples=1,
-            height=self.height,
-            width=self.width,
-            device=TorchDevice.choose_torch_device(),
-            dtype=inference_dtype,
-            seed=self.seed,
-        )
-
-        transformer_info = context.models.load(self.transformer.transformer)
-        is_schnell = "schnell" in transformer_info.config.config_path
-
-        # Calculate the timestep schedule.
-        image_seq_len = noise.shape[-1] * noise.shape[-2] // 4
-        timesteps = get_schedule(
-            num_steps=self.num_steps,
-            image_seq_len=image_seq_len,
-            shift=not is_schnell,
-        )
-
-        # Clip the timesteps schedule based on denoising_start and denoising_end.
-        timesteps = clip_timestep_schedule(timesteps, self.denoising_start, self.denoising_end)
-
-        # Prepare input latent image.
-        if init_latents is not None:
-            # If init_latents is provided, we are doing image-to-image.
-
-            if is_schnell:
-                context.logger.warning(
-                    "Running image-to-image with a FLUX schnell model. This is not recommended. The results are likely "
-                    "to be poor. Consider using a FLUX dev model instead."
-                )
-
-            # Noise the orig_latents by the appropriate amount for the first timestep.
-            t_0 = timesteps[0]
-            x = t_0 * noise + (1.0 - t_0) * init_latents
-        else:
-            # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
-            if self.denoising_start > 1e-5:
-                raise ValueError("denoising_start should be 0 when initial latents are not provided.")
-
-            x = noise
-
-        # If len(timesteps) == 1, then short-circuit. We are just noising the input latents, but not taking any
-        # denoising steps.
-        if len(timesteps) <= 1:
-            return x
-
-        inpaint_mask = self._prep_inpaint_mask(context, x)
-
-        b, _c, h, w = x.shape
-        img_ids = generate_img_ids(h=h, w=w, batch_size=b, device=x.device, dtype=x.dtype)
-
-        bs, t5_seq_len, _ = t5_embeddings.shape
-        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
-
-        # Pack all latent tensors.
-        init_latents = pack(init_latents) if init_latents is not None else None
-        inpaint_mask = pack(inpaint_mask) if inpaint_mask is not None else None
-        noise = pack(noise)
-        x = pack(x)
-
-        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
-        assert image_seq_len == x.shape[1]
-
-        # Prepare inpaint extension.
-        inpaint_extension: InpaintExtension | None = None
-        if inpaint_mask is not None:
-            assert init_latents is not None
-            inpaint_extension = InpaintExtension(
-                init_latents=init_latents,
-                inpaint_mask=inpaint_mask,
-                noise=noise,
-            )
-
-        with transformer_info as transformer:
-            assert isinstance(transformer, Flux)
-
-            x = denoise(
-                model=transformer,
-                img=x,
-                img_ids=img_ids,
-                txt=t5_embeddings,
-                txt_ids=txt_ids,
-                vec=clip_embeddings,
-                timesteps=timesteps,
-                step_callback=self._build_step_callback(context),
-                guidance=self.guidance,
-                inpaint_extension=inpaint_extension,
-            )
-
-        x = unpack(x.float(), self.height, self.width)
-        return x
-
-    def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
-        """Prepare the inpaint mask.
-
-        - Loads the mask
-        - Resizes if necessary
-        - Casts to same device/dtype as latents
-        - Expands mask to the same shape as latents so that they line up after 'packing'
-
-        Args:
-            context (InvocationContext): The invocation context, for loading the inpaint mask.
-            latents (torch.Tensor): A latent image tensor. In 'unpacked' format. Used to determine the target shape,
-                device, and dtype for the inpaint mask.
-
-        Returns:
-            torch.Tensor | None: Inpaint mask.
-        """
-        if self.denoise_mask is None:
-            return None
-
-        mask = context.tensors.load(self.denoise_mask.mask_name)
-
-        _, _, latent_height, latent_width = latents.shape
-        mask = tv_resize(
-            img=mask,
-            size=[latent_height, latent_width],
-            interpolation=tv_transforms.InterpolationMode.BILINEAR,
-            antialias=False,
-        )
-
-        mask = mask.to(device=latents.device, dtype=latents.dtype)
-
-        # Expand the inpaint mask to the same shape as `latents` so that when we 'pack' `mask` it lines up with
-        # `latents`.
-        return mask.expand_as(latents)
-
-    def _build_step_callback(self, context: InvocationContext) -> Callable[[PipelineIntermediateState], None]:
-        def step_callback(state: PipelineIntermediateState) -> None:
-            state.latents = unpack(state.latents.float(), self.height, self.width).squeeze()
-            context.util.flux_step_callback(state)
-
-        return step_callback

invokeai/app/invocations/flux_text_to_image.py

@@ -0,0 +1,169 @@
+import torch
+from einops import rearrange
+from PIL import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    FluxConditioningField,
+    Input,
+    InputField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import TransformerField, VAEField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.session_processor.session_processor_common import CanceledException
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.flux.sampling import denoise, get_noise, get_schedule, prepare_latent_img_patches, unpack
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "flux_text_to_image",
+    title="FLUX Text to Image",
+    tags=["image", "flux"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Text-to-image generation using a FLUX model."""
+
+    transformer: TransformerField = InputField(
+        description=FieldDescriptions.flux_model,
+        input=Input.Connection,
+        title="Transformer",
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    positive_text_conditioning: FluxConditioningField = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection
+    )
+    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
+    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
+    num_steps: int = InputField(
+        default=4, description="Number of diffusion steps. Recommend values are schnell: 4, dev: 50."
+    )
+    guidance: float = InputField(
+        default=4.0,
+        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
+    )
+    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = self._run_diffusion(context)
+        image = self._run_vae_decoding(context, latents)
+        image_dto = context.images.save(image=image)
+        return ImageOutput.build(image_dto)
+
+    def _run_diffusion(
+        self,
+        context: InvocationContext,
+    ):
+        inference_dtype = torch.bfloat16
+
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        flux_conditioning = cond_data.conditionings[0]
+        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+        flux_conditioning = flux_conditioning.to(dtype=inference_dtype)
+        t5_embeddings = flux_conditioning.t5_embeds
+        clip_embeddings = flux_conditioning.clip_embeds
+
+        transformer_info = context.models.load(self.transformer.transformer)
+
+        # Prepare input noise.
+        x = get_noise(
+            num_samples=1,
+            height=self.height,
+            width=self.width,
+            device=TorchDevice.choose_torch_device(),
+            dtype=inference_dtype,
+            seed=self.seed,
+        )
+
+        x, img_ids = prepare_latent_img_patches(x)
+
+        is_schnell = "schnell" in transformer_info.config.config_path
+
+        timesteps = get_schedule(
+            num_steps=self.num_steps,
+            image_seq_len=x.shape[1],
+            shift=not is_schnell,
+        )
+
+        bs, t5_seq_len, _ = t5_embeddings.shape
+        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
+
+        with transformer_info as transformer:
+            assert isinstance(transformer, Flux)
+
+            def step_callback() -> None:
+                if context.util.is_canceled():
+                    raise CanceledException
+
+                # TODO: Make this look like the image before re-enabling
+                # latent_image = unpack(img.float(), self.height, self.width)
+                # latent_image = latent_image.squeeze()  # Remove unnecessary dimensions
+                # flattened_tensor = latent_image.reshape(-1)  # Flatten to shape [48*128*128]
+
+                # # Create a new tensor of the required shape [255, 255, 3]
+                # latent_image = flattened_tensor[: 255 * 255 * 3].reshape(255, 255, 3)  # Reshape to RGB format
+
+                # # Convert to a NumPy array and then to a PIL Image
+                # image = Image.fromarray(latent_image.cpu().numpy().astype(np.uint8))
+
+                # (width, height) = image.size
+                # width *= 8
+                # height *= 8
+
+                # dataURL = image_to_dataURL(image, image_format="JPEG")
+
+                # # TODO: move this whole function to invocation context to properly reference these variables
+                # context._services.events.emit_invocation_denoise_progress(
+                #     context._data.queue_item,
+                #     context._data.invocation,
+                #     state,
+                #     ProgressImage(dataURL=dataURL, width=width, height=height),
+                # )
+
+            x = denoise(
+                model=transformer,
+                img=x,
+                img_ids=img_ids,
+                txt=t5_embeddings,
+                txt_ids=txt_ids,
+                vec=clip_embeddings,
+                timesteps=timesteps,
+                step_callback=step_callback,
+                guidance=self.guidance,
+            )
+
+        x = unpack(x.float(), self.height, self.width)
+
+        return x
+
+    def _run_vae_decoding(
+        self,
+        context: InvocationContext,
+        latents: torch.Tensor,
+    ) -> Image.Image:
+        vae_info = context.models.load(self.vae.vae)
+        with vae_info as vae:
+            assert isinstance(vae, AutoEncoder)
+            latents = latents.to(dtype=TorchDevice.choose_torch_dtype())
+            img = vae.decode(latents)
+
+        img = img.clamp(-1, 1)
+        img = rearrange(img[0], "c h w -> h w c")
+        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
+
+        return img_pil

invokeai/app/invocations/flux_vae_decode.py

@@ -1,60 +0,0 @@
-import torch
-from einops import rearrange
-from PIL import Image
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    Input,
-    InputField,
-    LatentsField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import VAEField
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
-from invokeai.backend.model_manager.load.load_base import LoadedModel
-from invokeai.backend.util.devices import TorchDevice
-
-
-@invocation(
-    "flux_vae_decode",
-    title="FLUX Latents to Image",
-    tags=["latents", "image", "vae", "l2i", "flux"],
-    category="latents",
-    version="1.0.0",
-)
-class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Generates an image from latents."""
-
-    latents: LatentsField = InputField(
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-
-    def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
-        with vae_info as vae:
-            assert isinstance(vae, AutoEncoder)
-            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype())
-            img = vae.decode(latents)
-
-        img = img.clamp(-1, 1)
-        img = rearrange(img[0], "c h w -> h w c")  # noqa: F821
-        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
-        return img_pil
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.tensors.load(self.latents.latents_name)
-        vae_info = context.models.load(self.vae.vae)
-        image = self._vae_decode(vae_info=vae_info, latents=latents)
-
-        TorchDevice.empty_cache()
-        image_dto = context.images.save(image=image)
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/flux_vae_encode.py

@@ -1,67 +0,0 @@
-import einops
-import torch
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    ImageField,
-    Input,
-    InputField,
-)
-from invokeai.app.invocations.model import VAEField
-from invokeai.app.invocations.primitives import LatentsOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
-from invokeai.backend.model_manager import LoadedModel
-from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
-from invokeai.backend.util.devices import TorchDevice
-
-
-@invocation(
-    "flux_vae_encode",
-    title="FLUX Image to Latents",
-    tags=["latents", "image", "vae", "i2l", "flux"],
-    category="latents",
-    version="1.0.0",
-)
-class FluxVaeEncodeInvocation(BaseInvocation):
-    """Encodes an image into latents."""
-
-    image: ImageField = InputField(
-        description="The image to encode.",
-    )
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-
-    @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        # TODO(ryand): Expose seed parameter at the invocation level.
-        # TODO(ryand): Write a util function for generating random tensors that is consistent across devices / dtypes.
-        # There's a starting point in get_noise(...), but it needs to be extracted and generalized. This function
-        # should be used for VAE encode sampling.
-        generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
-        with vae_info as vae:
-            assert isinstance(vae, AutoEncoder)
-            image_tensor = image_tensor.to(
-                device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
-            )
-            latents = vae.encode(image_tensor, sample=True, generator=generator)
-            return latents
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        image = context.images.get_pil(self.image.image_name)
-
-        vae_info = context.models.load(self.vae.vae)
-
-        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
-        if image_tensor.dim() == 3:
-            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
-
-        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
-
-        latents = latents.to("cpu")
-        name = context.tensors.save(tensor=latents)
-        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)

invokeai/app/invocations/mask.py

@@ -126,7 +126,7 @@ class ImageMaskToTensorInvocation(BaseInvocation, WithMetadata):
     title="Tensor Mask to Image",
     tags=["mask"],
     category="mask",
-    version="1.1.0",
+    version="1.0.0",
 )
 class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Convert a mask tensor to an image."""
@@ -135,11 +135,6 @@ class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         mask = context.tensors.load(self.mask.tensor_name)
-
-        # Squeeze the channel dimension if it exists.
-        if mask.dim() == 3:
-            mask = mask.squeeze(0)
-
         # Ensure that the mask is binary.
         if mask.dtype != torch.bool:
             mask = mask > 0.5

invokeai/app/services/model_install/model_install_common.py

@@ -103,7 +103,7 @@ class HFModelSource(StringLikeSource):
         if self.variant:
             base += f":{self.variant or ''}"
         if self.subfolder:
-            base += f"::{self.subfolder.as_posix()}"
+            base += f":{self.subfolder}"
         return base
 
 

invokeai/app/services/session_queue/session_queue_base.py

@@ -6,7 +6,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByDestinationResult,
+    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -97,8 +97,8 @@ class SessionQueueBase(ABC):
         pass
 
     @abstractmethod
-    def cancel_by_destination(self, queue_id: str, destination: str) -> CancelByDestinationResult:
-        """Cancels all queue items with the given batch destination"""
+    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
+        """Cancels all queue items with the given batch origin"""
         pass
 
     @abstractmethod

invokeai/app/services/session_queue/session_queue_common.py

@@ -346,10 +346,10 @@ class CancelByBatchIDsResult(BaseModel):
     canceled: int = Field(..., description="Number of queue items canceled")
 
 
-class CancelByDestinationResult(CancelByBatchIDsResult):
-    """Result of canceling by a destination"""
+class CancelByOriginResult(BaseModel):
+    """Result of canceling by list of batch ids"""
 
-    pass
+    canceled: int = Field(..., description="Number of queue items canceled")
 
 
 class CancelByQueueIDResult(CancelByBatchIDsResult):

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -10,7 +10,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByDestinationResult,
+    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -426,19 +426,19 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return CancelByBatchIDsResult(canceled=count)
 
-    def cancel_by_destination(self, queue_id: str, destination: str) -> CancelByDestinationResult:
+    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
         try:
             current_queue_item = self.get_current(queue_id)
             self.__lock.acquire()
             where = """--sql
                 WHERE
                   queue_id == ?
-                  AND destination == ?
+                  AND origin == ?
                   AND status != 'canceled'
                   AND status != 'completed'
                   AND status != 'failed'
                 """
-            params = (queue_id, destination)
+            params = (queue_id, origin)
             self.__cursor.execute(
                 f"""--sql
                 SELECT COUNT(*)
@@ -457,14 +457,14 @@ class SqliteSessionQueue(SessionQueueBase):
                 params,
             )
             self.__conn.commit()
-            if current_queue_item is not None and current_queue_item.destination == destination:
+            if current_queue_item is not None and current_queue_item.origin == origin:
                 self._set_queue_item_status(current_queue_item.item_id, "canceled")
         except Exception:
             self.__conn.rollback()
             raise
         finally:
             self.__lock.release()
-        return CancelByDestinationResult(canceled=count)
+        return CancelByOriginResult(canceled=count)
 
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         try:

invokeai/app/services/shared/invocation_context.py

@@ -14,7 +14,7 @@ from invokeai.app.services.image_records.image_records_common import ImageCatego
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.model_records.model_records_base import UnknownModelException
-from invokeai.app.util.step_callback import flux_step_callback, stable_diffusion_step_callback
+from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.model_manager.config import (
     AnyModel,
     AnyModelConfig,
@@ -557,24 +557,6 @@ class UtilInterface(InvocationContextInterface):
             is_canceled=self.is_canceled,
         )
 
-    def flux_step_callback(self, intermediate_state: PipelineIntermediateState) -> None:
-        """
-        The step callback emits a progress event with the current step, the total number of
-        steps, a preview image, and some other internal metadata.
-
-        This should be called after each denoising step.
-
-        Args:
-            intermediate_state: The intermediate state of the diffusion pipeline.
-        """
-
-        flux_step_callback(
-            context_data=self._data,
-            intermediate_state=intermediate_state,
-            events=self._services.events,
-            is_canceled=self.is_canceled,
-        )
-
 
 class InvocationContext:
     """Provides access to various services and data for the current invocation.

invokeai/app/services/workflow_records/default_workflows/FLUX Image to Image.json

@@ -1,407 +0,0 @@
-{
-  "name": "FLUX Image to Image",
-  "author": "InvokeAI",
-  "description": "A simple image-to-image workflow using a FLUX dev model. ",
-  "version": "1.0.4",
-  "contact": "",
-  "tags": "image2image, flux, image-to-image",
-  "notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend using FLUX dev models for image-to-image workflows. The image-to-image performance with FLUX schnell models is poor.",
-  "exposedFields": [
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "t5_encoder_model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "clip_embed_model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "vae_model"
-    },
-    {
-      "nodeId": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "fieldName": "denoising_start"
-    },
-    {
-      "nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "fieldName": "prompt"
-    },
-    {
-      "nodeId": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "fieldName": "num_steps"
-    }
-  ],
-  "meta": {
-    "version": "3.0.0",
-    "category": "default"
-  },
-  "nodes": [
-    {
-      "id": "2981a67c-480f-4237-9384-26b68dbf912b",
-      "type": "invocation",
-      "data": {
-        "id": "2981a67c-480f-4237-9384-26b68dbf912b",
-        "type": "flux_vae_encode",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": true,
-        "inputs": {
-          "image": {
-            "name": "image",
-            "label": "",
-            "value": {
-              "image_name": "8a5c62aa-9335-45d2-9c71-89af9fc1f8d4.png"
-            }
-          },
-          "vae": {
-            "name": "vae",
-            "label": ""
-          }
-        }
-      },
-      "position": {
-        "x": 732.7680166609682,
-        "y": -24.37398171806909
-      }
-    },
-    {
-      "id": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "type": "invocation",
-      "data": {
-        "id": "ace0258f-67d7-4eee-a218-6fff27065214",
-        "type": "flux_denoise",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "latents": {
-            "name": "latents",
-            "label": ""
-          },
-          "denoise_mask": {
-            "name": "denoise_mask",
-            "label": ""
-          },
-          "denoising_start": {
-            "name": "denoising_start",
-            "label": "",
-            "value": 0.04
-          },
-          "denoising_end": {
-            "name": "denoising_end",
-            "label": "",
-            "value": 1
-          },
-          "transformer": {
-            "name": "transformer",
-            "label": ""
-          },
-          "positive_text_conditioning": {
-            "name": "positive_text_conditioning",
-            "label": ""
-          },
-          "width": {
-            "name": "width",
-            "label": "",
-            "value": 1024
-          },
-          "height": {
-            "name": "height",
-            "label": "",
-            "value": 1024
-          },
-          "num_steps": {
-            "name": "num_steps",
-            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
-            "value": 30
-          },
-          "guidance": {
-            "name": "guidance",
-            "label": "",
-            "value": 4
-          },
-          "seed": {
-            "name": "seed",
-            "label": "",
-            "value": 0
-          }
-        }
-      },
-      "position": {
-        "x": 1182.8836633018684,
-        "y": -251.38882958913183
-      }
-    },
-    {
-      "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "type": "invocation",
-      "data": {
-        "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-        "type": "flux_vae_decode",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": false,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "latents": {
-            "name": "latents",
-            "label": ""
-          },
-          "vae": {
-            "name": "vae",
-            "label": ""
-          }
-        }
-      },
-      "position": {
-        "x": 1575.5797431839133,
-        "y": -209.00150975507415
-      }
-    },
-    {
-      "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "type": "invocation",
-      "data": {
-        "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-        "type": "flux_model_loader",
-        "version": "1.0.4",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": false,
-        "inputs": {
-          "model": {
-            "name": "model",
-            "label": "Model (dev variant recommended for Image-to-Image)"
-          },
-          "t5_encoder_model": {
-            "name": "t5_encoder_model",
-            "label": ""
-          },
-          "clip_embed_model": {
-            "name": "clip_embed_model",
-            "label": "",
-            "value": {
-              "key": "fa23a584-b623-415d-832a-21b5098ff1a1",
-              "hash": "blake3:17c19f0ef941c3b7609a9c94a659ca5364de0be364a91d4179f0e39ba17c3b70",
-              "name": "clip-vit-large-patch14",
-              "base": "any",
-              "type": "clip_embed"
-            }
-          },
-          "vae_model": {
-            "name": "vae_model",
-            "label": "",
-            "value": {
-              "key": "74fc82ba-c0a8-479d-a890-2126f82da758",
-              "hash": "blake3:ce21cb76364aa6e2421311cf4a4b5eb052a76c4f1cd207b50703d8978198a068",
-              "name": "FLUX.1-schnell_ae",
-              "base": "flux",
-              "type": "vae"
-            }
-          }
-        }
-      },
-      "position": {
-        "x": 328.1809894659957,
-        "y": -90.2241133566946
-      }
-    },
-    {
-      "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "type": "invocation",
-      "data": {
-        "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-        "type": "flux_text_encoder",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": true,
-        "inputs": {
-          "clip": {
-            "name": "clip",
-            "label": ""
-          },
-          "t5_encoder": {
-            "name": "t5_encoder",
-            "label": ""
-          },
-          "t5_max_seq_len": {
-            "name": "t5_max_seq_len",
-            "label": "T5 Max Seq Len",
-            "value": 256
-          },
-          "prompt": {
-            "name": "prompt",
-            "label": "",
-            "value": "a cat wearing a birthday hat"
-          }
-        }
-      },
-      "position": {
-        "x": 745.8823365057267,
-        "y": -299.60249175851914
-      }
-    },
-    {
-      "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
-      "type": "invocation",
-      "data": {
-        "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
-        "type": "rand_int",
-        "version": "1.0.1",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": false,
-        "inputs": {
-          "low": {
-            "name": "low",
-            "label": "",
-            "value": 0
-          },
-          "high": {
-            "name": "high",
-            "label": "",
-            "value": 2147483647
-          }
-        }
-      },
-      "position": {
-        "x": 725.834098928012,
-        "y": 496.2710031089931
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-      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
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-      "type": "default",
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-      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
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-      "type": "default",
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-      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
-      "sourceHandle": "conditioning",
-      "targetHandle": "positive_text_conditioning"
-    },
-    {
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-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "sourceHandle": "vae",
-      "targetHandle": "vae"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90max_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "sourceHandle": "max_seq_len",
-      "targetHandle": "t5_max_seq_len"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90t5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "sourceHandle": "t5_encoder",
-      "targetHandle": "t5_encoder"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90clip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "sourceHandle": "clip",
-      "targetHandle": "clip"
-    }
-  ]
-}

invokeai/app/services/workflow_records/default_workflows/Flux Text to Image.json

@@ -1,7 +1,7 @@
 {
   "name": "FLUX Text to Image",
   "author": "InvokeAI",
-  "description": "A simple text-to-image workflow using FLUX dev or schnell models.",
+  "description": "A simple text-to-image workflow using FLUX dev or schnell models. Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
   "version": "1.0.4",
   "contact": "",
   "tags": "text2image, flux",
@@ -11,25 +11,17 @@
       "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
       "fieldName": "model"
     },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "t5_encoder_model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "clip_embed_model"
-    },
-    {
-      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "fieldName": "vae_model"
-    },
     {
       "nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "fieldName": "prompt"
     },
     {
-      "nodeId": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "nodeId": "159bdf1b-79e7-4174-b86e-d40e646964c8",
       "fieldName": "num_steps"
+    },
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "t5_encoder_model"
     }
   ],
   "meta": {
@@ -37,121 +29,6 @@
     "category": "default"
   },
   "nodes": [
-    {
-      "id": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "type": "invocation",
-      "data": {
-        "id": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-        "type": "flux_denoise",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "latents": {
-            "name": "latents",
-            "label": ""
-          },
-          "denoise_mask": {
-            "name": "denoise_mask",
-            "label": ""
-          },
-          "denoising_start": {
-            "name": "denoising_start",
-            "label": "",
-            "value": 0
-          },
-          "denoising_end": {
-            "name": "denoising_end",
-            "label": "",
-            "value": 1
-          },
-          "transformer": {
-            "name": "transformer",
-            "label": ""
-          },
-          "positive_text_conditioning": {
-            "name": "positive_text_conditioning",
-            "label": ""
-          },
-          "width": {
-            "name": "width",
-            "label": "",
-            "value": 1024
-          },
-          "height": {
-            "name": "height",
-            "label": "",
-            "value": 1024
-          },
-          "num_steps": {
-            "name": "num_steps",
-            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
-            "value": 30
-          },
-          "guidance": {
-            "name": "guidance",
-            "label": "",
-            "value": 4
-          },
-          "seed": {
-            "name": "seed",
-            "label": "",
-            "value": 0
-          }
-        }
-      },
-      "position": {
-        "x": 1186.1868226120378,
-        "y": -214.9459927686657
-      }
-    },
-    {
-      "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "type": "invocation",
-      "data": {
-        "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-        "type": "flux_vae_decode",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": false,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "latents": {
-            "name": "latents",
-            "label": ""
-          },
-          "vae": {
-            "name": "vae",
-            "label": ""
-          }
-        }
-      },
-      "position": {
-        "x": 1575.5797431839133,
-        "y": -209.00150975507415
-      }
-    },
     {
       "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
       "type": "invocation",
@@ -222,8 +99,8 @@
         }
       },
       "position": {
-        "x": 778.4899149328337,
-        "y": -100.36469216659502
+        "x": 824.1970602278849,
+        "y": 146.98251001061735
       }
     },
     {
@@ -252,52 +129,77 @@
         }
       },
       "position": {
-        "x": 800.9667463219505,
-        "y": 285.8297267547506
+        "x": 822.9899179655476,
+        "y": 360.9657214885052
+      }
+    },
+    {
+      "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "type": "invocation",
+      "data": {
+        "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+        "type": "flux_text_to_image",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": false,
+        "useCache": true,
+        "inputs": {
+          "board": {
+            "name": "board",
+            "label": ""
+          },
+          "metadata": {
+            "name": "metadata",
+            "label": ""
+          },
+          "transformer": {
+            "name": "transformer",
+            "label": ""
+          },
+          "vae": {
+            "name": "vae",
+            "label": ""
+          },
+          "positive_text_conditioning": {
+            "name": "positive_text_conditioning",
+            "label": ""
+          },
+          "width": {
+            "name": "width",
+            "label": "",
+            "value": 1024
+          },
+          "height": {
+            "name": "height",
+            "label": "",
+            "value": 1024
+          },
+          "num_steps": {
+            "name": "num_steps",
+            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
+            "value": 30
+          },
+          "guidance": {
+            "name": "guidance",
+            "label": "",
+            "value": 4
+          },
+          "seed": {
+            "name": "seed",
+            "label": "",
+            "value": 0
+          }
+        }
+      },
+      "position": {
+        "x": 1216.3900791301849,
+        "y": 5.500841807102248
       }
     }
   ],
   "edges": [
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-4fe24f07-f906-4f55-ab2c-9beee56ef5bdtransformer",
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-      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "sourceHandle": "transformer",
-      "targetHandle": "transformer"
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-      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "sourceHandle": "conditioning",
-      "targetHandle": "positive_text_conditioning"
-    },
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-      "type": "default",
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-      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "sourceHandle": "value",
-      "targetHandle": "seed"
-    },
-    {
-      "id": "reactflow__edge-4fe24f07-f906-4f55-ab2c-9beee56ef5bdlatents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
-      "type": "default",
-      "source": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
-      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "sourceHandle": "latents",
-      "targetHandle": "latents"
-    },
-    {
-      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-7e5172eb-48c1-44db-a770-8fd83e1435d1vae",
-      "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
-      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
-      "sourceHandle": "vae",
-      "targetHandle": "vae"
-    },
     {
       "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90max_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
       "type": "default",
@@ -306,6 +208,14 @@
       "sourceHandle": "max_seq_len",
       "targetHandle": "t5_max_seq_len"
     },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-159bdf1b-79e7-4174-b86e-d40e646964c8vae",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    },
     {
       "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90t5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
       "type": "default",
@@ -321,6 +231,30 @@
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "clip",
       "targetHandle": "clip"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-159bdf1b-79e7-4174-b86e-d40e646964c8transformer",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "transformer",
+      "targetHandle": "transformer"
+    },
+    {
+      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-159bdf1b-79e7-4174-b86e-d40e646964c8positive_text_conditioning",
+      "type": "default",
+      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "conditioning",
+      "targetHandle": "positive_text_conditioning"
+    },
+    {
+      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-159bdf1b-79e7-4174-b86e-d40e646964c8seed",
+      "type": "default",
+      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "value",
+      "targetHandle": "seed"
     }
   ]
 }

invokeai/app/util/step_callback.py

@@ -38,25 +38,6 @@ SD1_5_LATENT_RGB_FACTORS = [
     [-0.1307, -0.1874, -0.7445],  # L4
 ]
 
-FLUX_LATENT_RGB_FACTORS = [
-    [-0.0412, 0.0149, 0.0521],
-    [0.0056, 0.0291, 0.0768],
-    [0.0342, -0.0681, -0.0427],
-    [-0.0258, 0.0092, 0.0463],
-    [0.0863, 0.0784, 0.0547],
-    [-0.0017, 0.0402, 0.0158],
-    [0.0501, 0.1058, 0.1152],
-    [-0.0209, -0.0218, -0.0329],
-    [-0.0314, 0.0083, 0.0896],
-    [0.0851, 0.0665, -0.0472],
-    [-0.0534, 0.0238, -0.0024],
-    [0.0452, -0.0026, 0.0048],
-    [0.0892, 0.0831, 0.0881],
-    [-0.1117, -0.0304, -0.0789],
-    [0.0027, -0.0479, -0.0043],
-    [-0.1146, -0.0827, -0.0598],
-]
-
 
 def sample_to_lowres_estimated_image(
     samples: torch.Tensor, latent_rgb_factors: torch.Tensor, smooth_matrix: Optional[torch.Tensor] = None
@@ -113,32 +94,3 @@ def stable_diffusion_step_callback(
         intermediate_state,
         ProgressImage(dataURL=dataURL, width=width, height=height),
     )
-
-
-def flux_step_callback(
-    context_data: "InvocationContextData",
-    intermediate_state: PipelineIntermediateState,
-    events: "EventServiceBase",
-    is_canceled: Callable[[], bool],
-) -> None:
-    if is_canceled():
-        raise CanceledException
-    sample = intermediate_state.latents
-    latent_rgb_factors = torch.tensor(FLUX_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
-    latent_image_perm = sample.permute(1, 2, 0).to(dtype=sample.dtype, device=sample.device)
-    latent_image = latent_image_perm @ latent_rgb_factors
-    latents_ubyte = (
-        ((latent_image + 1) / 2).clamp(0, 1).mul(0xFF)  # change scale from -1..1 to 0..1  # to 0..255
-    ).to(device="cpu", dtype=torch.uint8)
-    image = Image.fromarray(latents_ubyte.cpu().numpy())
-    (width, height) = image.size
-    width *= 8
-    height *= 8
-    dataURL = image_to_dataURL(image, image_format="JPEG")
-
-    events.emit_invocation_denoise_progress(
-        context_data.queue_item,
-        context_data.invocation,
-        intermediate_state,
-        ProgressImage(dataURL=dataURL, width=width, height=height),
-    )

invokeai/backend/flux/denoise.py

@@ -1,56 +0,0 @@
-from typing import Callable
-
-import torch
-from tqdm import tqdm
-
-from invokeai.backend.flux.inpaint_extension import InpaintExtension
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
-
-
-def denoise(
-    model: Flux,
-    # model input
-    img: torch.Tensor,
-    img_ids: torch.Tensor,
-    txt: torch.Tensor,
-    txt_ids: torch.Tensor,
-    vec: torch.Tensor,
-    # sampling parameters
-    timesteps: list[float],
-    step_callback: Callable[[PipelineIntermediateState], None],
-    guidance: float,
-    inpaint_extension: InpaintExtension | None,
-):
-    step = 0
-    # guidance_vec is ignored for schnell.
-    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
-    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
-        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
-        pred = model(
-            img=img,
-            img_ids=img_ids,
-            txt=txt,
-            txt_ids=txt_ids,
-            y=vec,
-            timesteps=t_vec,
-            guidance=guidance_vec,
-        )
-        preview_img = img - t_curr * pred
-        img = img + (t_prev - t_curr) * pred
-
-        if inpaint_extension is not None:
-            img = inpaint_extension.merge_intermediate_latents_with_init_latents(img, t_prev)
-
-        step_callback(
-            PipelineIntermediateState(
-                step=step,
-                order=1,
-                total_steps=len(timesteps),
-                timestep=int(t_curr),
-                latents=preview_img,
-            ),
-        )
-        step += 1
-
-    return img

invokeai/backend/flux/inpaint_extension.py

@@ -1,35 +0,0 @@
-import torch
-
-
-class InpaintExtension:
-    """A class for managing inpainting with FLUX."""
-
-    def __init__(self, init_latents: torch.Tensor, inpaint_mask: torch.Tensor, noise: torch.Tensor):
-        """Initialize InpaintExtension.
-
-        Args:
-            init_latents (torch.Tensor): The initial latents (i.e. un-noised at timestep 0). In 'packed' format.
-            inpaint_mask (torch.Tensor): A mask specifying which elements to inpaint. Range [0, 1]. Values of 1 will be
-                re-generated. Values of 0 will remain unchanged. Values between 0 and 1 can be used to blend the
-                inpainted region with the background. In 'packed' format.
-            noise (torch.Tensor): The noise tensor used to noise the init_latents. In 'packed' format.
-        """
-        assert init_latents.shape == inpaint_mask.shape == noise.shape
-        self._init_latents = init_latents
-        self._inpaint_mask = inpaint_mask
-        self._noise = noise
-
-    def merge_intermediate_latents_with_init_latents(
-        self, intermediate_latents: torch.Tensor, timestep: float
-    ) -> torch.Tensor:
-        """Merge the intermediate latents with the initial latents for the current timestep using the inpaint mask. I.e.
-        update the intermediate latents to keep the regions that are not being inpainted on the correct noise
-        trajectory.
-
-        This function should be called after each denoising step.
-        """
-        # Noise the init latents for the current timestep.
-        noised_init_latents = self._noise * timestep + (1.0 - timestep) * self._init_latents
-
-        # Merge the intermediate latents with the noised_init_latents using the inpaint_mask.
-        return intermediate_latents * self._inpaint_mask + noised_init_latents * (1.0 - self._inpaint_mask)

invokeai/backend/flux/modules/autoencoder.py

@@ -258,17 +258,16 @@ class Decoder(nn.Module):
 
 
 class DiagonalGaussian(nn.Module):
-    def __init__(self, chunk_dim: int = 1):
+    def __init__(self, sample: bool = True, chunk_dim: int = 1):
         super().__init__()
+        self.sample = sample
         self.chunk_dim = chunk_dim
 
-    def forward(self, z: Tensor, sample: bool = True, generator: torch.Generator | None = None) -> Tensor:
+    def forward(self, z: Tensor) -> Tensor:
         mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
-        if sample:
+        if self.sample:
             std = torch.exp(0.5 * logvar)
-            # Unfortunately, torch.randn_like(...) does not accept a generator argument at the time of writing, so we
-            # have to use torch.randn(...) instead.
-            return mean + std * torch.randn(size=mean.size(), generator=generator, dtype=mean.dtype, device=mean.device)
+            return mean + std * torch.randn_like(mean)
         else:
             return mean
 
@@ -298,21 +297,8 @@ class AutoEncoder(nn.Module):
         self.scale_factor = params.scale_factor
         self.shift_factor = params.shift_factor
 
-    def encode(self, x: Tensor, sample: bool = True, generator: torch.Generator | None = None) -> Tensor:
-        """Run VAE encoding on input tensor x.
-
-        Args:
-            x (Tensor): Input image tensor. Shape: (batch_size, in_channels, height, width).
-            sample (bool, optional): If True, sample from the encoded distribution, else, return the distribution mean.
-                Defaults to True.
-            generator (torch.Generator | None, optional): Optional random number generator for reproducibility.
-                Defaults to None.
-
-        Returns:
-            Tensor: Encoded latent tensor. Shape: (batch_size, z_channels, latent_height, latent_width).
-        """
-
-        z = self.reg(self.encoder(x), sample=sample, generator=generator)
+    def encode(self, x: Tensor) -> Tensor:
+        z = self.reg(self.encoder(x))
         z = self.scale_factor * (z - self.shift_factor)
         return z
 

invokeai/backend/flux/sampling.py

@@ -0,0 +1,167 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+import math
+from typing import Callable
+
+import torch
+from einops import rearrange, repeat
+from torch import Tensor
+from tqdm import tqdm
+
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.modules.conditioner import HFEncoder
+
+
+def get_noise(
+    num_samples: int,
+    height: int,
+    width: int,
+    device: torch.device,
+    dtype: torch.dtype,
+    seed: int,
+):
+    # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
+    rand_device = "cpu"
+    rand_dtype = torch.float16
+    return torch.randn(
+        num_samples,
+        16,
+        # allow for packing
+        2 * math.ceil(height / 16),
+        2 * math.ceil(width / 16),
+        device=rand_device,
+        dtype=rand_dtype,
+        generator=torch.Generator(device=rand_device).manual_seed(seed),
+    ).to(device=device, dtype=dtype)
+
+
+def prepare(t5: HFEncoder, clip: HFEncoder, img: Tensor, prompt: str | list[str]) -> dict[str, Tensor]:
+    bs, c, h, w = img.shape
+    if bs == 1 and not isinstance(prompt, str):
+        bs = len(prompt)
+
+    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img.shape[0] == 1 and bs > 1:
+        img = repeat(img, "1 ... -> bs ...", bs=bs)
+
+    img_ids = torch.zeros(h // 2, w // 2, 3)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    if isinstance(prompt, str):
+        prompt = [prompt]
+    txt = t5(prompt)
+    if txt.shape[0] == 1 and bs > 1:
+        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
+    txt_ids = torch.zeros(bs, txt.shape[1], 3)
+
+    vec = clip(prompt)
+    if vec.shape[0] == 1 and bs > 1:
+        vec = repeat(vec, "1 ... -> bs ...", bs=bs)
+
+    return {
+        "img": img,
+        "img_ids": img_ids.to(img.device),
+        "txt": txt.to(img.device),
+        "txt_ids": txt_ids.to(img.device),
+        "vec": vec.to(img.device),
+    }
+
+
+def time_shift(mu: float, sigma: float, t: Tensor):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+
+def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
+    m = (y2 - y1) / (x2 - x1)
+    b = y1 - m * x1
+    return lambda x: m * x + b
+
+
+def get_schedule(
+    num_steps: int,
+    image_seq_len: int,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+    shift: bool = True,
+) -> list[float]:
+    # extra step for zero
+    timesteps = torch.linspace(1, 0, num_steps + 1)
+
+    # shifting the schedule to favor high timesteps for higher signal images
+    if shift:
+        # eastimate mu based on linear estimation between two points
+        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
+        timesteps = time_shift(mu, 1.0, timesteps)
+
+    return timesteps.tolist()
+
+
+def denoise(
+    model: Flux,
+    # model input
+    img: Tensor,
+    img_ids: Tensor,
+    txt: Tensor,
+    txt_ids: Tensor,
+    vec: Tensor,
+    # sampling parameters
+    timesteps: list[float],
+    step_callback: Callable[[], None],
+    guidance: float = 4.0,
+):
+    # guidance_vec is ignored for schnell.
+    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
+    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
+        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
+        pred = model(
+            img=img,
+            img_ids=img_ids,
+            txt=txt,
+            txt_ids=txt_ids,
+            y=vec,
+            timesteps=t_vec,
+            guidance=guidance_vec,
+        )
+
+        img = img + (t_prev - t_curr) * pred
+        step_callback()
+
+    return img
+
+
+def unpack(x: Tensor, height: int, width: int) -> Tensor:
+    return rearrange(
+        x,
+        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
+        h=math.ceil(height / 16),
+        w=math.ceil(width / 16),
+        ph=2,
+        pw=2,
+    )
+
+
+def prepare_latent_img_patches(latent_img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Convert an input image in latent space to patches for diffusion.
+
+    This implementation was extracted from:
+    https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/sampling.py#L32
+
+    Returns:
+        tuple[Tensor, Tensor]: (img, img_ids), as defined in the original flux repo.
+    """
+    bs, c, h, w = latent_img.shape
+
+    # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
+    img = rearrange(latent_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img.shape[0] == 1 and bs > 1:
+        img = repeat(img, "1 ... -> bs ...", bs=bs)
+
+    # Generate patch position ids.
+    img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device, dtype=img.dtype)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device, dtype=img.dtype)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device, dtype=img.dtype)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    return img, img_ids

invokeai/backend/flux/sampling_utils.py

@@ -1,135 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-import math
-from typing import Callable
-
-import torch
-from einops import rearrange, repeat
-
-
-def get_noise(
-    num_samples: int,
-    height: int,
-    width: int,
-    device: torch.device,
-    dtype: torch.dtype,
-    seed: int,
-):
-    # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
-    rand_device = "cpu"
-    rand_dtype = torch.float16
-    return torch.randn(
-        num_samples,
-        16,
-        # allow for packing
-        2 * math.ceil(height / 16),
-        2 * math.ceil(width / 16),
-        device=rand_device,
-        dtype=rand_dtype,
-        generator=torch.Generator(device=rand_device).manual_seed(seed),
-    ).to(device=device, dtype=dtype)
-
-
-def time_shift(mu: float, sigma: float, t: torch.Tensor) -> torch.Tensor:
-    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
-
-
-def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
-    m = (y2 - y1) / (x2 - x1)
-    b = y1 - m * x1
-    return lambda x: m * x + b
-
-
-def get_schedule(
-    num_steps: int,
-    image_seq_len: int,
-    base_shift: float = 0.5,
-    max_shift: float = 1.15,
-    shift: bool = True,
-) -> list[float]:
-    # extra step for zero
-    timesteps = torch.linspace(1, 0, num_steps + 1)
-
-    # shifting the schedule to favor high timesteps for higher signal images
-    if shift:
-        # estimate mu based on linear estimation between two points
-        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
-        timesteps = time_shift(mu, 1.0, timesteps)
-
-    return timesteps.tolist()
-
-
-def _find_last_index_ge_val(timesteps: list[float], val: float, eps: float = 1e-6) -> int:
-    """Find the last index in timesteps that is >= val.
-
-    We use epsilon-close equality to avoid potential floating point errors.
-    """
-    idx = len(list(filter(lambda t: t >= (val - eps), timesteps))) - 1
-    assert idx >= 0
-    return idx
-
-
-def clip_timestep_schedule(timesteps: list[float], denoising_start: float, denoising_end: float) -> list[float]:
-    """Clip the timestep schedule to the denoising range.
-
-    Args:
-        timesteps (list[float]): The original timestep schedule: [1.0, ..., 0.0].
-        denoising_start (float): A value in [0, 1] specifying the start of the denoising process. E.g. a value of 0.2
-            would mean that the denoising process start at the last timestep in the schedule >= 0.8.
-        denoising_end (float): A value in [0, 1] specifying the end of the denoising process. E.g. a value of 0.8 would
-            mean that the denoising process end at the last timestep in the schedule >= 0.2.
-
-    Returns:
-        list[float]: The clipped timestep schedule.
-    """
-    assert 0.0 <= denoising_start <= 1.0
-    assert 0.0 <= denoising_end <= 1.0
-    assert denoising_start <= denoising_end
-
-    t_start_val = 1.0 - denoising_start
-    t_end_val = 1.0 - denoising_end
-
-    t_start_idx = _find_last_index_ge_val(timesteps, t_start_val)
-    t_end_idx = _find_last_index_ge_val(timesteps, t_end_val)
-
-    clipped_timesteps = timesteps[t_start_idx : t_end_idx + 1]
-
-    return clipped_timesteps
-
-
-def unpack(x: torch.Tensor, height: int, width: int) -> torch.Tensor:
-    """Unpack flat array of patch embeddings to latent image."""
-    return rearrange(
-        x,
-        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
-        h=math.ceil(height / 16),
-        w=math.ceil(width / 16),
-        ph=2,
-        pw=2,
-    )
-
-
-def pack(x: torch.Tensor) -> torch.Tensor:
-    """Pack latent image to flattented array of patch embeddings."""
-    # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
-    return rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-
-
-def generate_img_ids(h: int, w: int, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
-    """Generate tensor of image position ids.
-
-    Args:
-        h (int): Height of image in latent space.
-        w (int): Width of image in latent space.
-        batch_size (int): Batch size.
-        device (torch.device): Device.
-        dtype (torch.dtype): dtype.
-
-    Returns:
-        torch.Tensor: Image position ids.
-    """
-    img_ids = torch.zeros(h // 2, w // 2, 3, device=device, dtype=dtype)
-    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=device, dtype=dtype)[:, None]
-    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=device, dtype=dtype)[None, :]
-    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
-    return img_ids

invokeai/backend/model_manager/load/load_default.py

@@ -66,9 +66,8 @@ class ModelLoader(ModelLoaderBase):
         return (model_base / config.path).resolve()
 
     def _load_and_cache(self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> ModelLockerBase:
-        stats_name = ":".join([config.base, config.type, config.name, (submodel_type or "")])
         try:
-            return self._ram_cache.get(config.key, submodel_type, stats_name=stats_name)
+            return self._ram_cache.get(config.key, submodel_type)
         except IndexError:
             pass
 
@@ -85,7 +84,7 @@ class ModelLoader(ModelLoaderBase):
         return self._ram_cache.get(
             key=config.key,
             submodel_type=submodel_type,
-            stats_name=stats_name,
+            stats_name=":".join([config.base, config.type, config.name, (submodel_type or "")]),
         )
 
     def get_size_fs(

invokeai/backend/model_manager/load/model_cache/model_cache_base.py

@@ -128,24 +128,7 @@ class ModelCacheBase(ABC, Generic[T]):
     @property
     @abstractmethod
     def max_cache_size(self) -> float:
-        """Return the maximum size the RAM cache can grow to."""
-        pass
-
-    @max_cache_size.setter
-    @abstractmethod
-    def max_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-
-    @property
-    @abstractmethod
-    def max_vram_cache_size(self) -> float:
-        """Return the maximum size the VRAM cache can grow to."""
-        pass
-
-    @max_vram_cache_size.setter
-    @abstractmethod
-    def max_vram_cache_size(self, value: float) -> float:
-        """Set the maximum size the VRAM cache can grow to."""
+        """Return true if the cache is configured to lazily offload models in VRAM."""
         pass
 
     @abstractmethod

invokeai/backend/model_manager/load/model_cache/model_cache_default.py

@@ -70,7 +70,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
         max_vram_cache_size: float,
         execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
-        precision: torch.dtype = torch.float16,
         lazy_offloading: bool = True,
         log_memory_usage: bool = False,
         logger: Optional[Logger] = None,
@@ -82,13 +81,11 @@ class ModelCache(ModelCacheBase[AnyModel]):
         :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
         :param execution_device: Torch device to load active model into [torch.device('cuda')]
         :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
-        :param precision: Precision for loaded models [torch.float16]
-        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
+        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded.
         :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
             operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
             snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
             behaviour.
-        :param logger: InvokeAILogger to use (otherwise creates one)
         """
         # allow lazy offloading only when vram cache enabled
         self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
@@ -133,16 +130,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
         """Set the cap on cache size."""
         self._max_cache_size = value
 
-    @property
-    def max_vram_cache_size(self) -> float:
-        """Return the cap on vram cache size."""
-        return self._max_vram_cache_size
-
-    @max_vram_cache_size.setter
-    def max_vram_cache_size(self, value: float) -> None:
-        """Set the cap on vram cache size."""
-        self._max_vram_cache_size = value
-
     @property
     def stats(self) -> Optional[CacheStats]:
         """Return collected CacheStats object."""

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

@@ -32,9 +32,6 @@ from invokeai.backend.model_manager.config import (
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.model_manager.util.model_util import (
-    convert_bundle_to_flux_transformer_checkpoint,
-)
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 try:
@@ -193,13 +190,6 @@ class FluxCheckpointModel(ModelLoader):
         with SilenceWarnings():
             model = Flux(params[config.config_path])
             sd = load_file(model_path)
-            if "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in sd:
-                sd = convert_bundle_to_flux_transformer_checkpoint(sd)
-            new_sd_size = sum([ten.nelement() * torch.bfloat16.itemsize for ten in sd.values()])
-            self._ram_cache.make_room(new_sd_size)
-            for k in sd.keys():
-                # We need to cast to bfloat16 due to it being the only currently supported dtype for inference
-                sd[k] = sd[k].to(torch.bfloat16)
             model.load_state_dict(sd, assign=True)
         return model
 
@@ -240,7 +230,5 @@ class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
                 model = Flux(params[config.config_path])
                 model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=torch.bfloat16)
             sd = load_file(model_path)
-            if "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in sd:
-                sd = convert_bundle_to_flux_transformer_checkpoint(sd)
             model.load_state_dict(sd, assign=True)
         return model

invokeai/backend/model_manager/probe.py

@@ -108,8 +108,6 @@ class ModelProbe(object):
         "CLIPVisionModelWithProjection": ModelType.CLIPVision,
         "T2IAdapter": ModelType.T2IAdapter,
         "CLIPModel": ModelType.CLIPEmbed,
-        "CLIPTextModel": ModelType.CLIPEmbed,
-        "T5EncoderModel": ModelType.T5Encoder,
     }
 
     @classmethod
@@ -226,18 +224,7 @@ class ModelProbe(object):
         ckpt = ckpt.get("state_dict", ckpt)
 
         for key in [str(k) for k in ckpt.keys()]:
-            if key.startswith(
-                (
-                    "cond_stage_model.",
-                    "first_stage_model.",
-                    "model.diffusion_model.",
-                    # FLUX models in the official BFL format contain keys with the "double_blocks." prefix.
-                    "double_blocks.",
-                    # Some FLUX checkpoint files contain transformer keys prefixed with "model.diffusion_model".
-                    # This prefix is typically used to distinguish between multiple models bundled in a single file.
-                    "model.diffusion_model.double_blocks.",
-                )
-            ):
+            if key.startswith(("cond_stage_model.", "first_stage_model.", "model.diffusion_model.", "double_blocks.")):
                 # Keys starting with double_blocks are associated with Flux models
                 return ModelType.Main
             elif key.startswith(("encoder.conv_in", "decoder.conv_in")):
@@ -296,16 +283,9 @@ class ModelProbe(object):
         if (folder_path / "image_encoder.txt").exists():
             return ModelType.IPAdapter
 
-        config_path = None
-        for p in [
-            folder_path / "model_index.json",  # pipeline
-            folder_path / "config.json",  # most diffusers
-            folder_path / "text_encoder_2" / "config.json",  # T5 text encoder
-            folder_path / "text_encoder" / "config.json",  # T5 CLIP
-        ]:
-            if p.exists():
-                config_path = p
-                break
+        i = folder_path / "model_index.json"
+        c = folder_path / "config.json"
+        config_path = i if i.exists() else c if c.exists() else None
 
         if config_path:
             with open(config_path, "r") as file:
@@ -348,10 +328,7 @@ class ModelProbe(object):
                 # TODO: Decide between dev/schnell
                 checkpoint = ModelProbe._scan_and_load_checkpoint(model_path)
                 state_dict = checkpoint.get("state_dict") or checkpoint
-                if (
-                    "guidance_in.out_layer.weight" in state_dict
-                    or "model.diffusion_model.guidance_in.out_layer.weight" in state_dict
-                ):
+                if "guidance_in.out_layer.weight" in state_dict:
                     # For flux, this is a key in invokeai.backend.flux.util.params
                     #   Due to model type and format being the descriminator for model configs this
                     #   is used rather than attempting to support flux with separate model types and format
@@ -359,7 +336,7 @@ class ModelProbe(object):
                     config_file = "flux-dev"
                 else:
                     # For flux, this is a key in invokeai.backend.flux.util.params
-                    #   Due to model type and format being the discriminator for model configs this
+                    #   Due to model type and format being the descriminator for model configs this
                     #   is used rather than attempting to support flux with separate model types and format
                     #   If changed in the future, please fix me
                     config_file = "flux-schnell"
@@ -466,10 +443,7 @@ class CheckpointProbeBase(ProbeBase):
 
     def get_format(self) -> ModelFormat:
         state_dict = self.checkpoint.get("state_dict") or self.checkpoint
-        if (
-            "double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict
-            or "model.diffusion_model.double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict
-        ):
+        if "double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict:
             return ModelFormat.BnbQuantizednf4b
         return ModelFormat("checkpoint")
 
@@ -496,10 +470,7 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
         state_dict = self.checkpoint.get("state_dict") or checkpoint
-        if (
-            "double_blocks.0.img_attn.norm.key_norm.scale" in state_dict
-            or "model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale" in state_dict
-        ):
+        if "double_blocks.0.img_attn.norm.key_norm.scale" in state_dict:
             return BaseModelType.Flux
         key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         if key_name in state_dict and state_dict[key_name].shape[-1] == 768:
@@ -776,27 +747,8 @@ class TextualInversionFolderProbe(FolderProbeBase):
 
 
 class T5EncoderFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        return BaseModelType.Any
-
     def get_format(self) -> ModelFormat:
-        path = self.model_path / "text_encoder_2"
-        if (path / "model.safetensors.index.json").exists():
-            return ModelFormat.T5Encoder
-        files = list(path.glob("*.safetensors"))
-        if len(files) == 0:
-            raise InvalidModelConfigException(f"{self.model_path.as_posix()}: no .safetensors files found")
-
-        # shortcut: look for the quantization in the name
-        if any(x for x in files if "llm_int8" in x.as_posix()):
-            return ModelFormat.BnbQuantizedLlmInt8b
-
-        # more reliable path: probe contents for a 'SCB' key
-        ckpt = read_checkpoint_meta(files[0], scan=True)
-        if any("SCB" in x for x in ckpt.keys()):
-            return ModelFormat.BnbQuantizedLlmInt8b
-
-        raise InvalidModelConfigException(f"{self.model_path.as_posix()}: unknown model format")
+        return ModelFormat.T5Encoder
 
 
 class ONNXFolderProbe(PipelineFolderProbe):

invokeai/backend/model_manager/util/model_util.py

@@ -133,29 +133,3 @@ def lora_token_vector_length(checkpoint: Dict[str, torch.Tensor]) -> Optional[in
             break
 
     return lora_token_vector_length
-
-
-def convert_bundle_to_flux_transformer_checkpoint(
-    transformer_state_dict: dict[str, torch.Tensor],
-) -> dict[str, torch.Tensor]:
-    original_state_dict: dict[str, torch.Tensor] = {}
-    keys_to_remove: list[str] = []
-
-    for k, v in transformer_state_dict.items():
-        if not k.startswith("model.diffusion_model"):
-            keys_to_remove.append(k)  # This can be removed in the future if we only want to delete transformer keys
-            continue
-        if k.endswith("scale"):
-            # Scale math must be done at bfloat16 due to our current flux model
-            # support limitations at inference time
-            v = v.to(dtype=torch.bfloat16)
-        new_key = k.replace("model.diffusion_model.", "")
-        original_state_dict[new_key] = v
-        keys_to_remove.append(k)
-
-    # Remove processed keys from the original dictionary, leaving others in case
-    # other model state dicts need to be pulled
-    for k in keys_to_remove:
-        del transformer_state_dict[k]
-
-    return original_state_dict

invokeai/frontend/web/knip.ts

@@ -11,8 +11,6 @@ const config: KnipConfig = {
     'src/features/nodes/types/v2/**',
     // TODO(psyche): maybe we can clean up these utils after canvas v2 release
     'src/features/controlLayers/konva/util.ts',
-    // TODO(psyche): restore HRF functionality?
-    'src/features/hrf/**',
   ],
   ignoreBinaries: ['only-allow'],
   paths: {

invokeai/frontend/web/package.json

@@ -136,7 +136,6 @@
     "@vitest/coverage-v8": "^1.5.0",
     "@vitest/ui": "^1.5.0",
     "concurrently": "^8.2.2",
-    "csstype": "^3.1.3",
     "dpdm": "^3.14.0",
     "eslint": "^8.57.0",
     "eslint-plugin-i18next": "^6.0.9",

invokeai/frontend/web/pnpm-lock.yaml

@@ -238,9 +238,6 @@ devDependencies:
   concurrently:
     specifier: ^8.2.2
     version: 8.2.2
-  csstype:
-    specifier: ^3.1.3
-    version: 3.1.3
   dpdm:
     specifier: ^3.14.0
     version: 3.14.0

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

@@ -127,14 +127,7 @@
         "bulkDownloadRequestedDesc": "Dein Download wird vorbereitet. Dies kann ein paar Momente dauern.",
         "bulkDownloadRequestFailed": "Problem beim Download vorbereiten",
         "bulkDownloadFailed": "Download fehlgeschlagen",
-        "alwaysShowImageSizeBadge": "Zeige immer Bilder Größe Abzeichen",
-        "selectForCompare": "Zum Vergleichen auswählen",
-        "compareImage": "Bilder vergleichen",
-        "exitSearch": "Suche beenden",
-        "newestFirst": "Neueste zuerst",
-        "oldestFirst": "Älteste zuerst",
-        "openInViewer": "Im Viewer öffnen",
-        "swapImages": "Bilder tauschen"
+        "alwaysShowImageSizeBadge": "Zeige immer Bilder Größe Abzeichen"
     },
     "hotkeys": {
         "keyboardShortcuts": "Tastenkürzel",
@@ -638,8 +631,7 @@
         "archived": "Archiviert",
         "noBoards": "Kein {boardType}} Ordner",
         "hideBoards": "Ordner verstecken",
-        "viewBoards": "Ordner ansehen",
-        "deletedPrivateBoardsCannotbeRestored": "Gelöschte Boards können nicht wiederhergestellt werden. Wenn Sie „Nur Board löschen“ wählen, werden die Bilder in einen privaten, nicht kategorisierten Status für den Ersteller des Bildes versetzt."
+        "viewBoards": "Ordner ansehen"
     },
     "controlnet": {
         "showAdvanced": "Zeige Erweitert",
@@ -789,9 +781,7 @@
         "batchFieldValues": "Stapelverarbeitungswerte",
         "batchQueued": "Stapelverarbeitung eingereiht",
         "graphQueued": "Graph eingereiht",
-        "graphFailedToQueue": "Fehler beim Einreihen des Graphen",
-        "generations_one": "Generation",
-        "generations_other": "Generationen"
+        "graphFailedToQueue": "Fehler beim Einreihen des Graphen"
     },
     "metadata": {
         "negativePrompt": "Negativ Beschreibung",
@@ -1156,10 +1146,5 @@
         "noMatchingTriggers": "Keine passenden Trigger",
         "addPromptTrigger": "Prompt-Trigger hinzufügen",
         "compatibleEmbeddings": "Kompatible Einbettungen"
-    },
-    "ui": {
-        "tabs": {
-            "queue": "Warteschlange"
-        }
     }
 }

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

@@ -134,7 +134,6 @@
         "nodes": "Workflows",
         "notInstalled": "Not $t(common.installed)",
         "openInNewTab": "Open in New Tab",
-        "openInViewer": "Open in Viewer",
         "orderBy": "Order By",
         "outpaint": "outpaint",
         "outputs": "Outputs",
@@ -1015,8 +1014,6 @@
             "noModelForControlAdapter": "Control Adapter #{{number}} has no model selected.",
             "incompatibleBaseModelForControlAdapter": "Control Adapter #{{number}} model is incompatible with main model.",
             "noModelSelected": "No model selected",
-            "canvasManagerNotLoaded": "Canvas Manager not loaded",
-            "canvasBusy": "Canvas is busy",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
             "systemDisconnected": "System disconnected",
@@ -1052,7 +1049,8 @@
         "seamlessYAxis": "Seamless Tiling Y Axis",
         "seed": "Seed",
         "imageActions": "Image Actions",
-        "sendToCanvas": "Send To Canvas",
+        "sendToImg2Img": "Send to Image to Image",
+        "sendToUnifiedCanvas": "Send To Unified Canvas",
         "sendToUpscale": "Send To Upscale",
         "showOptionsPanel": "Show Side Panel (O or T)",
         "shuffle": "Shuffle Seed",
@@ -1193,8 +1191,8 @@
         "problemSavingMaskDesc": "Unable to export mask",
         "prunedQueue": "Pruned Queue",
         "resetInitialImage": "Reset Initial Image",
-        "sentToCanvas": "Sent to Canvas",
-        "sentToUpscale": "Sent to Upscale",
+        "sentToImageToImage": "Sent To Image To Image",
+        "sentToUnifiedCanvas": "Sent to Unified Canvas",
         "serverError": "Server Error",
         "sessionRef": "Session: {{sessionId}}",
         "setAsCanvasInitialImage": "Set as canvas initial image",
@@ -1656,9 +1654,6 @@
         "storeNotInitialized": "Store is not initialized"
     },
     "controlLayers": {
-        "bookmark": "Bookmark for Quick Switch",
-        "fitBboxToLayers": "Fit Bbox To Layers",
-        "removeBookmark": "Remove Bookmark",
         "saveCanvasToGallery": "Save Canvas To Gallery",
         "saveBboxToGallery": "Save Bbox To Gallery",
         "savedToGalleryOk": "Saved to Gallery",
@@ -1677,7 +1672,6 @@
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
-        "compositeMaskedRegions": "Composite Masked Regions",
         "addLayer": "Add Layer",
         "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
@@ -1731,12 +1725,12 @@
         "regionalGuidance_withCount_hidden": "Regional Guidance ({{count}} hidden)",
         "controlLayers_withCount_hidden": "Control Layers ({{count}} hidden)",
         "rasterLayers_withCount_hidden": "Raster Layers ({{count}} hidden)",
-        "globalIPAdapters_withCount_hidden": "Global IP Adapters ({{count}} hidden)",
+        "ipAdapters_withCount_hidden": "IP Adapters ({{count}} hidden)",
         "inpaintMasks_withCount_hidden": "Inpaint Masks ({{count}} hidden)",
         "regionalGuidance_withCount_visible": "Regional Guidance ({{count}})",
         "controlLayers_withCount_visible": "Control Layers ({{count}})",
         "rasterLayers_withCount_visible": "Raster Layers ({{count}})",
-        "globalIPAdapters_withCount_visible": "Global IP Adapters ({{count}})",
+        "ipAdapters_withCount_visible": "IP Adapters ({{count}})",
         "inpaintMasks_withCount_visible": "Inpaint Masks ({{count}})",
         "globalControlAdapter": "Global $t(controlnet.controlAdapter_one)",
         "globalControlAdapterLayer": "Global $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
@@ -1749,8 +1743,8 @@
         "clearProcessor": "Clear Processor",
         "resetProcessor": "Reset Processor to Defaults",
         "noLayersAdded": "No Layers Added",
-        "layer_one": "Layer",
-        "layer_other": "Layers",
+        "layers_one": "Layer",
+        "layers_other": "Layers",
         "objects_zero": "empty",
         "objects_one": "{{count}} object",
         "objects_other": "{{count}} objects",
@@ -1786,6 +1780,7 @@
             "bbox": "Bbox",
             "move": "Move",
             "view": "View",
+            "transform": "Transform",
             "colorPicker": "Color Picker"
         },
         "filter": {
@@ -1795,13 +1790,6 @@
             "preview": "Preview",
             "apply": "Apply",
             "cancel": "Cancel"
-        },
-        "transform": {
-            "transform": "Transform",
-            "fitToBbox": "Fit to Bbox",
-            "reset": "Reset",
-            "apply": "Apply",
-            "cancel": "Cancel"
         }
     },
     "upscaling": {

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

@@ -86,15 +86,15 @@
         "loadMore": "Cargar más",
         "noImagesInGallery": "No hay imágenes para mostrar",
         "deleteImage_one": "Eliminar Imagen",
-        "deleteImage_many": "Eliminar {{count}} Imágenes",
-        "deleteImage_other": "Eliminar {{count}} Imágenes",
+        "deleteImage_many": "",
+        "deleteImage_other": "",
         "deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
         "assets": "Activos",
         "autoAssignBoardOnClick": "Asignación automática de tableros al hacer clic"
     },
     "hotkeys": {
         "keyboardShortcuts": "Atajos de teclado",
-        "appHotkeys": "Atajos de aplicación",
+        "appHotkeys": "Atajos de applicación",
         "generalHotkeys": "Atajos generales",
         "galleryHotkeys": "Atajos de galería",
         "unifiedCanvasHotkeys": "Atajos de lienzo unificado",
@@ -535,7 +535,7 @@
         "bottomMessage": "Al eliminar este panel y las imágenes que contiene, se restablecerán las funciones que los estén utilizando actualmente.",
         "deleteBoardAndImages": "Borrar el panel y las imágenes",
         "loading": "Cargando...",
-        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al Seleccionar 'Borrar Solo el Panel' transferirá las imágenes a un estado sin categorizar.",
+        "deletedBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar",
         "move": "Mover",
         "menuItemAutoAdd": "Agregar automáticamente a este panel",
         "searchBoard": "Buscando paneles…",
@@ -549,13 +549,7 @@
         "imagesWithCount_other": "{{count}} imágenes",
         "assetsWithCount_one": "{{count}} activo",
         "assetsWithCount_many": "{{count}} activos",
-        "assetsWithCount_other": "{{count}} activos",
-        "hideBoards": "Ocultar Paneles",
-        "addPrivateBoard": "Agregar un tablero privado",
-        "addSharedBoard": "Agregar Panel Compartido",
-        "boards": "Paneles",
-        "archiveBoard": "Archivar Panel",
-        "archived": "Archivado"
+        "assetsWithCount_other": "{{count}} activos"
     },
     "accordions": {
         "compositing": {

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

@@ -496,9 +496,7 @@
         "main": "Principali",
         "noModelsInstalledDesc1": "Installa i modelli con",
         "ipAdapters": "Adattatori IP",
-        "noMatchingModels": "Nessun modello corrispondente",
-        "starterModelsInModelManager": "I modelli iniziali possono essere trovati in Gestione Modelli",
-        "spandrelImageToImage": "Immagine a immagine (Spandrel)"
+        "noMatchingModels": "Nessun modello corrispondente"
     },
     "parameters": {
         "images": "Immagini",
@@ -512,7 +510,7 @@
         "perlinNoise": "Rumore Perlin",
         "type": "Tipo",
         "strength": "Forza",
-        "upscaling": "Amplia",
+        "upscaling": "Ampliamento",
         "scale": "Scala",
         "imageFit": "Adatta l'immagine iniziale alle dimensioni di output",
         "scaleBeforeProcessing": "Scala prima dell'elaborazione",
@@ -595,7 +593,7 @@
         "globalPositivePromptPlaceholder": "Prompt positivo globale",
         "globalNegativePromptPlaceholder": "Prompt negativo globale",
         "processImage": "Elabora Immagine",
-        "sendToUpscale": "Invia a Amplia",
+        "sendToUpscale": "Invia a Ampliare",
         "postProcessing": "Post-elaborazione (Shift + U)"
     },
     "settings": {
@@ -1422,7 +1420,7 @@
         "paramUpscaleMethod": {
             "heading": "Metodo di ampliamento",
             "paragraphs": [
-                "Metodo utilizzato per ampliare l'immagine per la correzione ad alta risoluzione."
+                "Metodo utilizzato per eseguire l'ampliamento dell'immagine per la correzione ad alta risoluzione."
             ]
         },
         "patchmatchDownScaleSize": {
@@ -1530,7 +1528,7 @@
         },
         "upscaleModel": {
             "paragraphs": [
-                "Il modello di ampliamento, scala l'immagine alle dimensioni di uscita prima di aggiungere i dettagli. È possibile utilizzare qualsiasi modello di ampliamento supportato, ma alcuni sono specializzati per diversi tipi di immagini, come foto o disegni al tratto."
+                "Il modello di ampliamento (Upscale), scala l'immagine alle dimensioni di uscita prima di aggiungere i dettagli. È possibile utilizzare qualsiasi modello di ampliamento supportato, ma alcuni sono specializzati per diversi tipi di immagini, come foto o disegni al tratto."
             ],
             "heading": "Modello di ampliamento"
         },
@@ -1722,27 +1720,26 @@
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
             "queue": "Coda",
             "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
-            "upscaling": "Amplia",
+            "upscaling": "Ampliamento",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
     },
     "upscaling": {
         "creativity": "Creatività",
         "structure": "Struttura",
-        "upscaleModel": "Modello di ampliamento",
+        "upscaleModel": "Modello di Ampliamento",
         "scale": "Scala",
         "missingModelsWarning": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare i modelli richiesti:",
         "mainModelDesc": "Modello principale (architettura SD1.5 o SDXL)",
         "tileControlNetModelDesc": "Modello Tile ControlNet per l'architettura del modello principale scelto",
-        "upscaleModelDesc": "Modello per l'ampliamento (immagine a immagine)",
+        "upscaleModelDesc": "Modello per l'ampliamento (da immagine a immagine)",
         "missingUpscaleInitialImage": "Immagine iniziale mancante per l'ampliamento",
         "missingUpscaleModel": "Modello per l’ampliamento mancante",
         "missingTileControlNetModel": "Nessun modello ControlNet Tile valido installato",
         "postProcessingModel": "Modello di post-elaborazione",
         "postProcessingMissingModelWarning": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare un modello di post-elaborazione (da immagine a immagine).",
         "exceedsMaxSize": "Le impostazioni di ampliamento superano il limite massimo delle dimensioni",
-        "exceedsMaxSizeDetails": "Il limite massimo di ampliamento è {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixel. Prova un'immagine più piccola o diminuisci la scala selezionata.",
-        "upscale": "Amplia"
+        "exceedsMaxSizeDetails": "Il limite massimo di ampliamento è {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixel. Prova un'immagine più piccola o diminuisci la scala selezionata."
     },
     "upsell": {
         "inviteTeammates": "Invita collaboratori",
@@ -1792,7 +1789,6 @@
         "positivePromptColumn": "'prompt' o 'positive_prompt'",
         "noTemplates": "Nessun modello",
         "acceptedColumnsKeys": "Colonne/chiavi accettate:",
-        "templateActions": "Azioni modello",
-        "promptTemplateCleared": "Modello di prompt cancellato"
+        "templateActions": "Azioni modello"
     }
 }

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

@@ -501,8 +501,7 @@
         "noModelsInstalled": "Нет установленных моделей",
         "noModelsInstalledDesc1": "Установите модели с помощью",
         "noMatchingModels": "Нет подходящих моделей",
-        "ipAdapters": "IP адаптеры",
-        "starterModelsInModelManager": "Стартовые модели можно найти в Менеджере моделей"
+        "ipAdapters": "IP адаптеры"
     },
     "parameters": {
         "images": "Изображения",
@@ -1759,8 +1758,7 @@
         "postProcessingModel": "Модель постобработки",
         "tileControlNetModelDesc": "Модель ControlNet для выбранной архитектуры основной модели",
         "missingModelsWarning": "Зайдите в <LinkComponent>Менеджер моделей</LinkComponent> чтоб установить необходимые модели:",
-        "postProcessingMissingModelWarning": "Посетите <LinkComponent>Менеджер моделей</LinkComponent>, чтобы установить модель постобработки (img2img).",
-        "upscale": "Увеличить"
+        "postProcessingMissingModelWarning": "Посетите <LinkComponent>Менеджер моделей</LinkComponent>, чтобы установить модель постобработки (img2img)."
     },
     "stylePresets": {
         "noMatchingTemplates": "Нет подходящих шаблонов",
@@ -1806,8 +1804,7 @@
         "noTemplates": "Нет шаблонов",
         "promptTemplatesDesc2": "Используйте строку-заполнитель <Pre>{{placeholder}}</Pre>, чтобы указать место, куда должен быть включен ваш запрос в шаблоне.",
         "searchByName": "Поиск по имени",
-        "shared": "Общий",
-        "promptTemplateCleared": "Шаблон запроса создан"
+        "shared": "Общий"
     },
     "upsell": {
         "inviteTeammates": "Пригласите членов команды",

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

@@ -154,8 +154,7 @@
         "displaySearch": "显示搜索",
         "stretchToFit": "拉伸以适应",
         "exitCompare": "退出对比",
-        "compareHelp1": "在点击图库中的图片或使用箭头键切换比较图片时，请按住<Kbd>Alt</Kbd> 键。",
-        "go": "运行"
+        "compareHelp1": "在点击图库中的图片或使用箭头键切换比较图片时，请按住<Kbd>Alt</Kbd> 键。"
     },
     "hotkeys": {
         "keyboardShortcuts": "快捷键",
@@ -495,9 +494,7 @@
         "huggingFacePlaceholder": "所有者或模型名称",
         "huggingFaceRepoID": "HuggingFace仓库ID",
         "loraTriggerPhrases": "LoRA 触发词",
-        "ipAdapters": "IP适配器",
-        "spandrelImageToImage": "图生图(Spandrel)",
-        "starterModelsInModelManager": "您可以在模型管理器中找到初始模型"
+        "ipAdapters": "IP适配器"
     },
     "parameters": {
         "images": "图像",
@@ -698,9 +695,7 @@
         "outOfMemoryErrorDesc": "您当前的生成设置已超出系统处理能力.请调整设置后再次尝试.",
         "parametersSet": "参数已恢复",
         "errorCopied": "错误信息已复制",
-        "modelImportCanceled": "模型导入已取消",
-        "importFailed": "导入失败",
-        "importSuccessful": "导入成功"
+        "modelImportCanceled": "模型导入已取消"
     },
     "unifiedCanvas": {
         "layer": "图层",
@@ -1710,55 +1705,12 @@
         "missingModelsWarning": "请访问<LinkComponent>模型管理器</LinkComponent> 安装所需的模型：",
         "mainModelDesc": "主模型（SD1.5或SDXL架构）",
         "exceedsMaxSize": "放大设置超出了最大尺寸限制",
-        "exceedsMaxSizeDetails": "最大放大限制是 {{maxUpscaleDimension}}x{{maxUpscaleDimension}} 像素.请尝试一个较小的图像或减少您的缩放选择.",
-        "upscale": "放大"
+        "exceedsMaxSizeDetails": "最大放大限制是 {{maxUpscaleDimension}}x{{maxUpscaleDimension}} 像素.请尝试一个较小的图像或减少您的缩放选择."
     },
     "upsell": {
         "inviteTeammates": "邀请团队成员",
         "professional": "专业",
         "professionalUpsell": "可在 Invoke 的专业版中使用.点击此处或访问 invoke.com/pricing 了解更多详情.",
         "shareAccess": "共享访问权限"
-    },
-    "stylePresets": {
-        "positivePrompt": "正向提示词",
-        "preview": "预览",
-        "deleteImage": "删除图像",
-        "deleteTemplate": "删除模版",
-        "deleteTemplate2": "您确定要删除这个模板吗？请注意，删除后无法恢复.",
-        "importTemplates": "导入提示模板，支持CSV或JSON格式",
-        "insertPlaceholder": "插入一个占位符",
-        "myTemplates": "我的模版",
-        "name": "名称",
-        "type": "类型",
-        "unableToDeleteTemplate": "无法删除提示模板",
-        "updatePromptTemplate": "更新提示词模版",
-        "exportPromptTemplates": "导出我的提示模板为CSV格式",
-        "exportDownloaded": "导出已下载",
-        "noMatchingTemplates": "无匹配的模版",
-        "promptTemplatesDesc1": "提示模板可以帮助您在编写提示时添加预设的文本内容.",
-        "promptTemplatesDesc3": "如果您没有使用占位符，那么模板的内容将会被添加到您提示的末尾.",
-        "searchByName": "按名称搜索",
-        "shared": "已分享",
-        "sharedTemplates": "已分享的模版",
-        "templateActions": "模版操作",
-        "templateDeleted": "提示模版已删除",
-        "toggleViewMode": "切换显示模式",
-        "uploadImage": "上传图像",
-        "active": "激活",
-        "choosePromptTemplate": "选择提示词模板",
-        "clearTemplateSelection": "清除模版选择",
-        "copyTemplate": "拷贝模版",
-        "createPromptTemplate": "创建提示词模版",
-        "defaultTemplates": "默认模版",
-        "editTemplate": "编辑模版",
-        "exportFailed": "无法生成并下载CSV文件",
-        "flatten": "将选定的模板内容合并到当前提示中",
-        "negativePrompt": "反向提示词",
-        "promptTemplateCleared": "提示模板已清除",
-        "useForTemplate": "用于提示词模版",
-        "viewList": "预览模版列表",
-        "viewModeTooltip": "这是您的提示在当前选定的模板下的预览效果。如需编辑提示，请直接在文本框中点击进行修改.",
-        "noTemplates": "无模版",
-        "private": "私密"
     }
 }

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

@@ -1,11 +1,10 @@
-import { isAnyOf } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import {
   sessionStagingAreaImageAccepted,
   sessionStagingAreaReset,
 } from 'features/controlLayers/store/canvasSessionSlice';
-import { canvasReset, rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
+import { rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
 import { imageDTOToImageObject } from 'features/controlLayers/store/types';
@@ -17,16 +16,14 @@ import { assert } from 'tsafe';
 
 const log = logger('canvas');
 
-const matchCanvasOrStagingAreaRest = isAnyOf(sessionStagingAreaReset, canvasReset);
-
 export const addStagingListeners = (startAppListening: AppStartListening) => {
   startAppListening({
-    matcher: matchCanvasOrStagingAreaRest,
+    actionCreator: sessionStagingAreaReset,
     effect: async (_, { dispatch }) => {
       try {
         const req = dispatch(
-          queueApi.endpoints.cancelByBatchDestination.initiate(
-            { destination: 'canvas' },
+          queueApi.endpoints.cancelByBatchOrigin.initiate(
+            { origin: 'canvas' },
             { fixedCacheKey: 'cancelByBatchOrigin' }
           )
         );

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

@@ -31,7 +31,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
 
       let didStartStaging = false;
 
-      if (!state.canvasSession.isStaging && state.canvasSettings.sendToCanvas) {
+      if (!state.canvasSession.isStaging && state.canvasSession.sendToCanvas) {
         dispatch(sessionStartedStaging());
         didStartStaging = true;
       }
@@ -70,7 +70,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
 
       const { g, noise, posCond } = buildGraphResult.value;
 
-      const destination = state.canvasSettings.sendToCanvas ? 'canvas' : 'gallery';
+      const destination = state.canvasSession.sendToCanvas ? 'canvas' : 'gallery';
 
       const prepareBatchResult = withResult(() =>
         prepareLinearUIBatch(state, g, prepend, noise, posCond, 'generation', destination)

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

@@ -13,7 +13,7 @@ import { loraDeleted } from 'features/controlLayers/store/lorasSlice';
 import { modelChanged, refinerModelChanged, vaeSelected } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import { getEntityIdentifier } from 'features/controlLayers/store/types';
-import { calculateNewSize } from 'features/parameters/components/Bbox/calculateNewSize';
+import { calculateNewSize } from 'features/parameters/components/DocumentSize/calculateNewSize';
 import { postProcessingModelChanged, upscaleModelChanged } from 'features/parameters/store/upscaleSlice';
 import { zParameterModel, zParameterVAEModel } from 'features/parameters/types/parameterSchemas';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';

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

@@ -11,6 +11,7 @@ import { canvasSettingsPersistConfig, canvasSettingsSlice } from 'features/contr
 import { canvasPersistConfig, canvasSlice, canvasUndoableConfig } from 'features/controlLayers/store/canvasSlice';
 import { lorasPersistConfig, lorasSlice } from 'features/controlLayers/store/lorasSlice';
 import { paramsPersistConfig, paramsSlice } from 'features/controlLayers/store/paramsSlice';
+import { toolPersistConfig, toolSlice } from 'features/controlLayers/store/toolSlice';
 import { deleteImageModalSlice } from 'features/deleteImageModal/store/slice';
 import { dynamicPromptsPersistConfig, dynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { galleryPersistConfig, gallerySlice } from 'features/gallery/store/gallerySlice';
@@ -62,6 +63,7 @@ const allReducers = {
   [upscaleSlice.name]: upscaleSlice.reducer,
   [stylePresetSlice.name]: stylePresetSlice.reducer,
   [paramsSlice.name]: paramsSlice.reducer,
+  [toolSlice.name]: toolSlice.reducer,
   [canvasSettingsSlice.name]: canvasSettingsSlice.reducer,
   [canvasSessionSlice.name]: canvasSessionSlice.reducer,
   [lorasSlice.name]: lorasSlice.reducer,
@@ -107,6 +109,7 @@ const persistConfigs: { [key in keyof typeof allReducers]?: PersistConfig } = {
   [upscalePersistConfig.name]: upscalePersistConfig,
   [stylePresetPersistConfig.name]: stylePresetPersistConfig,
   [paramsPersistConfig.name]: paramsPersistConfig,
+  [toolPersistConfig.name]: toolPersistConfig,
   [canvasSettingsPersistConfig.name]: canvasSettingsPersistConfig,
   [canvasSessionPersistConfig.name]: canvasSessionPersistConfig,
   [lorasPersistConfig.name]: lorasPersistConfig,

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

@@ -2,7 +2,6 @@ import { useStore } from '@nanostores/react';
 import { $isConnected } from 'app/hooks/useSocketIO';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { useCanvasManagerSafe } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import { selectDynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
@@ -18,7 +17,6 @@ import { selectSystemSlice } from 'features/system/store/systemSlice';
 import { selectActiveTab } from 'features/ui/store/uiSelectors';
 import i18n from 'i18next';
 import { forEach, upperFirst } from 'lodash-es';
-import { atom } from 'nanostores';
 import { useMemo } from 'react';
 import { getConnectedEdges } from 'reactflow';
 
@@ -30,7 +28,7 @@ const LAYER_TYPE_TO_TKEY = {
   control_layer: 'controlLayers.globalControlAdapter',
 } as const;
 
-const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy: boolean) =>
+const createSelector = (templates: Templates, isConnected: boolean) =>
   createMemoizedSelector(
     [
       selectSystemSlice,
@@ -119,10 +117,6 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
           reasons.push({ content: i18n.t('upscaling.missingTileControlNetModel') });
         }
       } else {
-        if (canvasIsBusy) {
-          reasons.push({ content: i18n.t('parameters.invoke.canvasBusy') });
-        }
-
         if (dynamicPrompts.prompts.length === 0 && getShouldProcessPrompt(positivePrompt)) {
           reasons.push({ content: i18n.t('parameters.invoke.noPrompts') });
         }
@@ -134,7 +128,7 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
         canvas.controlLayers.entities
           .filter((controlLayer) => controlLayer.isEnabled)
           .forEach((controlLayer, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY['control_layer']);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
@@ -164,7 +158,7 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
         canvas.ipAdapters.entities
           .filter((entity) => entity.isEnabled)
           .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
@@ -192,7 +186,7 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
         canvas.regions.entities
           .filter((entity) => entity.isEnabled)
           .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
@@ -229,7 +223,7 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
         canvas.rasterLayers.entities
           .filter((entity) => entity.isEnabled)
           .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
@@ -246,17 +240,10 @@ const createSelector = (templates: Templates, isConnected: boolean, canvasIsBusy
     }
   );
 
-const dummyAtom = atom(true);
-
 export const useIsReadyToEnqueue = () => {
   const templates = useStore($templates);
   const isConnected = useStore($isConnected);
-  const canvasManager = useCanvasManagerSafe();
-  const canvasIsBusy = useStore(canvasManager?.$isBusy ?? dummyAtom);
-  const selector = useMemo(
-    () => createSelector(templates, isConnected, canvasIsBusy),
-    [templates, isConnected, canvasIsBusy]
-  );
+  const selector = useMemo(() => createSelector(templates, isConnected), [templates, isConnected]);
   const value = useAppSelector(selector);
   return value;
 };

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

@@ -1,9 +1,6 @@
 export const roundDownToMultiple = (num: number, multiple: number): number => {
   return Math.floor(num / multiple) * multiple;
 };
-export const roundUpToMultiple = (num: number, multiple: number): number => {
-  return Math.ceil(num / multiple) * multiple;
-};
 
 export const roundToMultiple = (num: number, multiple: number): number => {
   return Math.round(num / multiple) * multiple;

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

@@ -46,7 +46,7 @@ export const CanvasAddEntityButtons = memo(() => {
           {t('controlLayers.controlLayer')}
         </Button>
         <Button variant="ghost" justifyContent="flex-start" leftIcon={<PiPlusBold />} onClick={addIPAdapter}>
-          {t('controlLayers.globalIPAdapter')}
+          {t('controlLayers.ipAdapter')}
         </Button>
       </ButtonGroup>
     </Flex>

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

@@ -0,0 +1,20 @@
+import { Flex, Spacer } from '@invoke-ai/ui-library';
+import { EntityListActionBarAddLayerButton } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarAddLayerMenuButton';
+import { EntityListActionBarDeleteButton } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarDeleteButton';
+import { EntityListActionBarSelectedEntityFill } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarSelectedEntityFill';
+import { SelectedEntityOpacity } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarSelectedEntityOpacity';
+import { memo } from 'react';
+
+export const EntityListActionBar = memo(() => {
+  return (
+    <Flex w="full" py={1} px={1} gap={2} alignItems="center">
+      <SelectedEntityOpacity />
+      <Spacer />
+      <EntityListActionBarSelectedEntityFill />
+      <EntityListActionBarAddLayerButton />
+      <EntityListActionBarDeleteButton />
+    </Flex>
+  );
+});
+
+EntityListActionBar.displayName = 'EntityListActionBar';

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

@@ -0,0 +1,28 @@
+import { IconButton, Menu, MenuButton, MenuList } from '@invoke-ai/ui-library';
+import { CanvasEntityListMenuItems } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarAddLayerMenuItems';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiPlusBold } from 'react-icons/pi';
+
+export const EntityListActionBarAddLayerButton = memo(() => {
+  const { t } = useTranslation();
+
+  return (
+    <Menu>
+      <MenuButton
+        as={IconButton}
+        size="sm"
+        tooltip={t('controlLayers.addLayer')}
+        aria-label={t('controlLayers.addLayer')}
+        icon={<PiPlusBold />}
+        variant="ghost"
+        data-testid="control-layers-add-layer-menu-button"
+      />
+      <MenuList>
+        <CanvasEntityListMenuItems />
+      </MenuList>
+    </Menu>
+  );
+});
+
+EntityListActionBarAddLayerButton.displayName = 'EntityListActionBarAddLayerButton';

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

@@ -0,0 +1,54 @@
+import { MenuItem } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import {
+  controlLayerAdded,
+  inpaintMaskAdded,
+  ipaAdded,
+  rasterLayerAdded,
+  rgAdded,
+} from 'features/controlLayers/store/canvasSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiPlusBold } from 'react-icons/pi';
+
+export const CanvasEntityListMenuItems = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const addInpaintMask = useCallback(() => {
+    dispatch(inpaintMaskAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addRegionalGuidance = useCallback(() => {
+    dispatch(rgAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addRasterLayer = useCallback(() => {
+    dispatch(rasterLayerAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addControlLayer = useCallback(() => {
+    dispatch(controlLayerAdded({ isSelected: true }));
+  }, [dispatch]);
+  const addIPAdapter = useCallback(() => {
+    dispatch(ipaAdded({ isSelected: true }));
+  }, [dispatch]);
+
+  return (
+    <>
+      <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask}>
+        {t('controlLayers.inpaintMask')}
+      </MenuItem>
+      <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance}>
+        {t('controlLayers.regionalGuidance')}
+      </MenuItem>
+      <MenuItem icon={<PiPlusBold />} onClick={addRasterLayer}>
+        {t('controlLayers.rasterLayer')}
+      </MenuItem>
+      <MenuItem icon={<PiPlusBold />} onClick={addControlLayer}>
+        {t('controlLayers.controlLayer')}
+      </MenuItem>
+      <MenuItem icon={<PiPlusBold />} onClick={addIPAdapter}>
+        {t('controlLayers.ipAdapter')}
+      </MenuItem>
+    </>
+  );
+});
+
+CanvasEntityListMenuItems.displayName = 'CanvasEntityListMenu';

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

@@ -0,0 +1,39 @@
+import { IconButton, useShiftModifier } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { allEntitiesDeleted, entityDeleted } from 'features/controlLayers/store/canvasSlice';
+import { selectEntityCount, selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiTrashSimpleFill } from 'react-icons/pi';
+
+export const EntityListActionBarDeleteButton = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
+  const entityCount = useAppSelector(selectEntityCount);
+  const shift = useShiftModifier();
+  const onClick = useCallback(() => {
+    if (shift) {
+      dispatch(allEntitiesDeleted());
+      return;
+    }
+    if (!selectedEntityIdentifier) {
+      return;
+    }
+    dispatch(entityDeleted({ entityIdentifier: selectedEntityIdentifier }));
+  }, [dispatch, selectedEntityIdentifier, shift]);
+
+  return (
+    <IconButton
+      onClick={onClick}
+      isDisabled={shift ? entityCount === 0 : !selectedEntityIdentifier}
+      size="sm"
+      variant="ghost"
+      aria-label={shift ? t('controlLayers.deleteAll') : t('controlLayers.deleteSelected')}
+      tooltip={shift ? t('controlLayers.deleteAll') : t('controlLayers.deleteSelected')}
+      icon={<PiTrashSimpleFill />}
+    />
+  );
+});
+
+EntityListActionBarDeleteButton.displayName = 'EntityListActionBarDeleteButton';

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

@@ -9,7 +9,7 @@ import { type FillStyle, isMaskEntityIdentifier, type RgbColor } from 'features/
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-export const EntityListSelectedEntityActionBarFill = memo(() => {
+export const EntityListActionBarSelectedEntityFill = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
@@ -67,4 +67,4 @@ export const EntityListSelectedEntityActionBarFill = memo(() => {
   );
 });
 
-EntityListSelectedEntityActionBarFill.displayName = 'EntityListSelectedEntityActionBarFill';
+EntityListActionBarSelectedEntityFill.displayName = 'EntityListActionBarSelectedEntityFill';

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

@@ -22,7 +22,7 @@ import {
   selectEntity,
   selectSelectedEntityIdentifier,
 } from 'features/controlLayers/store/selectors';
-import { isRenderableEntity } from 'features/controlLayers/store/types';
+import { isDrawableEntity } from 'features/controlLayers/store/types';
 import { clamp, round } from 'lodash-es';
 import type { KeyboardEvent } from 'react';
 import { memo, useCallback, useEffect, useState } from 'react';
@@ -37,11 +37,11 @@ function formatPct(v: number | string) {
   return `${round(Number(v), 2).toLocaleString()}%`;
 }
 
-function mapSliderValueToRawValue(value: number) {
+function mapSliderValueToOpacity(value: number) {
   return value / 100;
 }
 
-function mapRawValueToSliderValue(opacity: number) {
+function mapOpacityToSliderValue(opacity: number) {
   return opacity * 100;
 }
 
@@ -50,14 +50,14 @@ function formatSliderValue(value: number) {
 }
 
 const marks = [
-  mapRawValueToSliderValue(0),
-  mapRawValueToSliderValue(0.25),
-  mapRawValueToSliderValue(0.5),
-  mapRawValueToSliderValue(0.75),
-  mapRawValueToSliderValue(1),
+  mapOpacityToSliderValue(0),
+  mapOpacityToSliderValue(0.25),
+  mapOpacityToSliderValue(0.5),
+  mapOpacityToSliderValue(0.75),
+  mapOpacityToSliderValue(1),
 ];
 
-const sliderDefaultValue = mapRawValueToSliderValue(1);
+const sliderDefaultValue = mapOpacityToSliderValue(1);
 
 const snapCandidates = marks.slice(1, marks.length - 1);
 
@@ -70,14 +70,14 @@ const selectOpacity = createSelector(selectCanvasSlice, (canvas) => {
   if (!selectedEntity) {
     return 1; // fallback to 100% opacity
   }
-  if (!isRenderableEntity(selectedEntity)) {
+  if (!isDrawableEntity(selectedEntity)) {
     return 1; // fallback to 100% opacity
   }
   // Opacity is a float from 0-1, but we want to display it as a percentage
   return selectedEntity.opacity;
 });
 
-export const EntityListSelectedEntityActionBarOpacity = memo(() => {
+export const SelectedEntityOpacity = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
@@ -95,7 +95,7 @@ export const EntityListSelectedEntityActionBarOpacity = memo(() => {
       if (!$shift.get()) {
         snappedOpacity = snapToNearest(opacity, snapCandidates, 2);
       }
-      const mappedOpacity = mapSliderValueToRawValue(snappedOpacity);
+      const mappedOpacity = mapSliderValueToOpacity(snappedOpacity);
 
       dispatch(entityOpacityChanged({ entityIdentifier: selectedEntityIdentifier, opacity: mappedOpacity }));
     },
@@ -193,4 +193,4 @@ export const EntityListSelectedEntityActionBarOpacity = memo(() => {
   );
 });
 
-EntityListSelectedEntityActionBarOpacity.displayName = 'EntityListSelectedEntityActionBarOpacity';
+SelectedEntityOpacity.displayName = 'SelectedEntityOpacity';

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

@@ -1,20 +0,0 @@
-import { Flex, Spacer } from '@invoke-ai/ui-library';
-import { EntityListGlobalActionBarAddLayerMenu } from 'features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBarAddLayerMenu';
-import { EntityListGlobalActionBarDenoisingStrength } from 'features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBarDenoisingStrength';
-import { EntityListGlobalActionBarFitBboxToLayers } from 'features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBarFitBboxToLayers';
-import { memo } from 'react';
-
-export const EntityListGlobalActionBar = memo(() => {
-  return (
-    <Flex w="full" py={1} px={1} gap={2} alignItems="center">
-      <EntityListGlobalActionBarDenoisingStrength />
-      <Spacer />
-      <Flex>
-        <EntityListGlobalActionBarFitBboxToLayers />
-        <EntityListGlobalActionBarAddLayerMenu />
-      </Flex>
-    </Flex>
-  );
-});
-
-EntityListGlobalActionBar.displayName = 'EntityListGlobalActionBar';

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

@@ -1,69 +0,0 @@
-import { IconButton, Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { useDefaultIPAdapter } from 'features/controlLayers/hooks/useLayerControlAdapter';
-import {
-  controlLayerAdded,
-  inpaintMaskAdded,
-  ipaAdded,
-  rasterLayerAdded,
-  rgAdded,
-} from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiPlusBold } from 'react-icons/pi';
-
-export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const defaultIPAdapter = useDefaultIPAdapter();
-  const addInpaintMask = useCallback(() => {
-    dispatch(inpaintMaskAdded({ isSelected: true }));
-  }, [dispatch]);
-  const addRegionalGuidance = useCallback(() => {
-    dispatch(rgAdded({ isSelected: true }));
-  }, [dispatch]);
-  const addRasterLayer = useCallback(() => {
-    dispatch(rasterLayerAdded({ isSelected: true }));
-  }, [dispatch]);
-  const addControlLayer = useCallback(() => {
-    dispatch(controlLayerAdded({ isSelected: true }));
-  }, [dispatch]);
-  const addIPAdapter = useCallback(() => {
-    const overrides = { ipAdapter: defaultIPAdapter };
-    dispatch(ipaAdded({ isSelected: true, overrides }));
-  }, [defaultIPAdapter, dispatch]);
-
-  return (
-    <Menu>
-      <MenuButton
-        as={IconButton}
-        size="sm"
-        variant="link"
-        alignSelf="stretch"
-        tooltip={t('controlLayers.addLayer')}
-        aria-label={t('controlLayers.addLayer')}
-        icon={<PiPlusBold />}
-        data-testid="control-layers-add-layer-menu-button"
-      />
-      <MenuList>
-        <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask}>
-          {t('controlLayers.inpaintMask')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance}>
-          {t('controlLayers.regionalGuidance')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addRasterLayer}>
-          {t('controlLayers.rasterLayer')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addControlLayer}>
-          {t('controlLayers.controlLayer')}
-        </MenuItem>
-        <MenuItem icon={<PiPlusBold />} onClick={addIPAdapter}>
-          {t('controlLayers.globalIPAdapter')}
-        </MenuItem>
-      </MenuList>
-    </Menu>
-  );
-});
-
-EntityListGlobalActionBarAddLayerMenu.displayName = 'EntityListGlobalActionBarAddLayerMenu';

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

@@ -1,124 +0,0 @@
-import {
-  CompositeSlider,
-  FormControl,
-  FormLabel,
-  IconButton,
-  NumberInput,
-  NumberInputField,
-  Popover,
-  PopoverAnchor,
-  PopoverArrow,
-  PopoverBody,
-  PopoverContent,
-  PopoverTrigger,
-} from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
-import { selectImg2imgStrength, setImg2imgStrength } from 'features/controlLayers/store/paramsSlice';
-import { selectImg2imgStrengthConfig } from 'features/system/store/configSlice';
-import { clamp } from 'lodash-es';
-import type { KeyboardEvent } from 'react';
-import { memo, useCallback, useEffect, useState } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiCaretDownBold } from 'react-icons/pi';
-
-const marks = [0, 0.25, 0.5, 0.75, 1];
-
-export const EntityListGlobalActionBarDenoisingStrength = memo(() => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const strength = useAppSelector(selectImg2imgStrength);
-  const config = useAppSelector(selectImg2imgStrengthConfig);
-
-  const [localStrength, setLocalStrength] = useState(strength);
-
-  const onChangeSlider = useCallback(
-    (value: number) => {
-      dispatch(setImg2imgStrength(value));
-    },
-    [dispatch]
-  );
-
-  const onBlur = useCallback(() => {
-    if (isNaN(Number(localStrength))) {
-      setLocalStrength(config.initial);
-      return;
-    }
-    dispatch(setImg2imgStrength(clamp(localStrength, 0, 1)));
-  }, [config.initial, dispatch, localStrength]);
-
-  const onChangeNumberInput = useCallback((valueAsString: string, valueAsNumber: number) => {
-    setLocalStrength(valueAsNumber);
-  }, []);
-
-  const onKeyDown = useCallback(
-    (e: KeyboardEvent<HTMLInputElement>) => {
-      if (e.key === 'Enter') {
-        onBlur();
-      }
-    },
-    [onBlur]
-  );
-
-  useEffect(() => {
-    setLocalStrength(strength);
-  }, [strength]);
-
-  return (
-    <Popover>
-      <FormControl w="min-content" gap={2}>
-        <InformationalPopover feature="paramDenoisingStrength">
-          <FormLabel m={0}>{`${t('parameters.denoisingStrength')}`}</FormLabel>
-        </InformationalPopover>
-        <PopoverAnchor>
-          <NumberInput
-            display="flex"
-            alignItems="center"
-            step={config.coarseStep}
-            min={config.numberInputMin}
-            max={config.numberInputMax}
-            defaultValue={config.initial}
-            value={localStrength}
-            onChange={onChangeNumberInput}
-            onBlur={onBlur}
-            w="60px"
-            onKeyDown={onKeyDown}
-            clampValueOnBlur={false}
-            variant="outline"
-          >
-            <NumberInputField paddingInlineEnd={7} _focusVisible={{ zIndex: 0 }} />
-            <PopoverTrigger>
-              <IconButton
-                aria-label="open-slider"
-                icon={<PiCaretDownBold />}
-                size="sm"
-                variant="link"
-                position="absolute"
-                insetInlineEnd={0}
-                h="full"
-              />
-            </PopoverTrigger>
-          </NumberInput>
-        </PopoverAnchor>
-      </FormControl>
-      <PopoverContent w={200} pt={0} pb={2} px={4}>
-        <PopoverArrow />
-        <PopoverBody>
-          <CompositeSlider
-            step={config.coarseStep}
-            fineStep={config.fineStep}
-            min={config.sliderMin}
-            max={config.sliderMax}
-            defaultValue={config.initial}
-            onChange={onChangeSlider}
-            value={localStrength}
-            marks={marks}
-            alwaysShowMarks
-          />
-        </PopoverBody>
-      </PopoverContent>
-    </Popover>
-  );
-});
-
-EntityListGlobalActionBarDenoisingStrength.displayName = 'EntityListGlobalActionBarDenoisingStrength';

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

@@ -1,27 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiArrowsOut } from 'react-icons/pi';
-
-export const EntityListGlobalActionBarFitBboxToLayers = memo(() => {
-  const { t } = useTranslation();
-  const canvasManager = useCanvasManager();
-  const onClick = useCallback(() => {
-    canvasManager.bbox.fitToLayers();
-  }, [canvasManager.bbox]);
-
-  return (
-    <IconButton
-      onClick={onClick}
-      size="sm"
-      variant="link"
-      alignSelf="stretch"
-      aria-label={t('controlLayers.fitBboxToLayers')}
-      tooltip={t('controlLayers.fitBboxToLayers')}
-      icon={<PiArrowsOut />}
-    />
-  );
-});
-
-EntityListGlobalActionBarFitBboxToLayers.displayName = 'EntityListGlobalActionBarFitBboxToLayers';

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

@@ -1,24 +0,0 @@
-import { Flex, Spacer } from '@invoke-ai/ui-library';
-import { EntityListSelectedEntityActionBarDuplicateButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarDuplicateButton';
-import { EntityListSelectedEntityActionBarFill } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarFill';
-import { EntityListSelectedEntityActionBarFilterButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarFilterButton';
-import { EntityListSelectedEntityActionBarOpacity } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarOpacity';
-import { EntityListSelectedEntityActionBarTransformButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarTransformButton';
-import { memo } from 'react';
-
-export const EntityListSelectedEntityActionBar = memo(() => {
-  return (
-    <Flex w="full" py={1} px={1} gap={2} alignItems="center">
-      <EntityListSelectedEntityActionBarOpacity />
-      <Spacer />
-      <EntityListSelectedEntityActionBarFill />
-      <Flex>
-        <EntityListSelectedEntityActionBarFilterButton />
-        <EntityListSelectedEntityActionBarTransformButton />
-        <EntityListSelectedEntityActionBarDuplicateButton />
-      </Flex>
-    </Flex>
-  );
-});
-
-EntityListSelectedEntityActionBar.displayName = 'EntityListSelectedEntityActionBar';

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

@@ -1,34 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { entityDuplicated } from 'features/controlLayers/store/canvasSlice';
-import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiCopyFill } from 'react-icons/pi';
-
-export const EntityListSelectedEntityActionBarDuplicateButton = memo(() => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
-  const onClick = useCallback(() => {
-    if (!selectedEntityIdentifier) {
-      return;
-    }
-    dispatch(entityDuplicated({ entityIdentifier: selectedEntityIdentifier }));
-  }, [dispatch, selectedEntityIdentifier]);
-
-  return (
-    <IconButton
-      onClick={onClick}
-      isDisabled={!selectedEntityIdentifier}
-      size="sm"
-      variant="link"
-      alignSelf="stretch"
-      aria-label={t('controlLayers.duplicate')}
-      tooltip={t('controlLayers.duplicate')}
-      icon={<PiCopyFill />}
-    />
-  );
-});
-
-EntityListSelectedEntityActionBarDuplicateButton.displayName = 'EntityListSelectedEntityActionBarDuplicateButton';

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

@@ -1,52 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
-import { selectIsStaging } from 'features/controlLayers/store/canvasSessionSlice';
-import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import { isFilterableEntityIdentifier } from 'features/controlLayers/store/types';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiShootingStarBold } from 'react-icons/pi';
-
-export const EntityListSelectedEntityActionBarFilterButton = memo(() => {
-  const { t } = useTranslation();
-  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
-  const canvasManager = useCanvasManager();
-  const isStaging = useAppSelector(selectIsStaging);
-  const isBusy = useCanvasIsBusy();
-
-  const onClick = useCallback(() => {
-    if (!selectedEntityIdentifier) {
-      return;
-    }
-    if (!isFilterableEntityIdentifier(selectedEntityIdentifier)) {
-      return;
-    }
-
-    canvasManager.filter.startFilter(selectedEntityIdentifier);
-  }, [canvasManager, selectedEntityIdentifier]);
-
-  if (!selectedEntityIdentifier) {
-    return null;
-  }
-
-  if (!isFilterableEntityIdentifier(selectedEntityIdentifier)) {
-    return null;
-  }
-
-  return (
-    <IconButton
-      onClick={onClick}
-      isDisabled={isBusy || isStaging}
-      size="sm"
-      variant="link"
-      alignSelf="stretch"
-      aria-label={t('controlLayers.filter.filter')}
-      tooltip={t('controlLayers.filter.filter')}
-      icon={<PiShootingStarBold />}
-    />
-  );
-});
-
-EntityListSelectedEntityActionBarFilterButton.displayName = 'EntityListSelectedEntityActionBarFilterButton';

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

@@ -1,55 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
-import { selectIsStaging } from 'features/controlLayers/store/canvasSessionSlice';
-import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import { isTransformableEntityIdentifier } from 'features/controlLayers/store/types';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiFrameCornersBold } from 'react-icons/pi';
-
-export const EntityListSelectedEntityActionBarTransformButton = memo(() => {
-  const { t } = useTranslation();
-  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
-  const canvasManager = useCanvasManager();
-  const isStaging = useAppSelector(selectIsStaging);
-  const isBusy = useCanvasIsBusy();
-
-  const onClick = useCallback(() => {
-    if (!selectedEntityIdentifier) {
-      return;
-    }
-    if (!isTransformableEntityIdentifier(selectedEntityIdentifier)) {
-      return;
-    }
-    const adapter = canvasManager.getAdapter(selectedEntityIdentifier);
-    if (!adapter) {
-      return;
-    }
-    adapter.transformer.startTransform();
-  }, [canvasManager, selectedEntityIdentifier]);
-
-  if (!selectedEntityIdentifier) {
-    return null;
-  }
-
-  if (!isTransformableEntityIdentifier(selectedEntityIdentifier)) {
-    return null;
-  }
-
-  return (
-    <IconButton
-      onClick={onClick}
-      isDisabled={isBusy || isStaging}
-      size="sm"
-      variant="link"
-      alignSelf="stretch"
-      aria-label={t('controlLayers.transform.transform')}
-      tooltip={t('controlLayers.transform.transform')}
-      icon={<PiFrameCornersBold />}
-    />
-  );
-});
-
-EntityListSelectedEntityActionBarTransformButton.displayName = 'EntityListSelectedEntityActionBarTransformButton';

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

@@ -2,8 +2,7 @@ import { Divider, Flex } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 import { CanvasAddEntityButtons } from 'features/controlLayers/components/CanvasAddEntityButtons';
 import { CanvasEntityList } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityList';
-import { EntityListGlobalActionBar } from 'features/controlLayers/components/CanvasEntityList/EntityListGlobalActionBar';
-import { EntityListSelectedEntityActionBar } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBar';
+import { EntityListActionBar } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBar';
 import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { selectHasEntities } from 'features/controlLayers/store/selectors';
 import { memo } from 'react';
@@ -14,9 +13,7 @@ export const CanvasPanelContent = memo(() => {
   return (
     <CanvasManagerProviderGate>
       <Flex flexDir="column" gap={2} w="full" h="full">
-        <EntityListGlobalActionBar />
-        <Divider py={0} />
-        <EntityListSelectedEntityActionBar />
+        <EntityListActionBar />
         <Divider py={0} />
         {!hasEntities && <CanvasAddEntityButtons />}
         {hasEntities && <CanvasEntityList />}

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

@@ -1,4 +1,4 @@
-import { $alt, IconButton } from '@invoke-ai/ui-library';
+import { $shift, IconButton } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { INTERACTION_SCOPES } from 'common/hooks/interactionScopes';
 import { $canvasManager } from 'features/controlLayers/konva/CanvasManager';
@@ -7,7 +7,7 @@ import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiArrowCounterClockwiseBold } from 'react-icons/pi';
 
-export const CanvasToolbarResetViewButton = memo(() => {
+export const CanvasResetViewButton = memo(() => {
   const { t } = useTranslation();
   const canvasManager = useStore($canvasManager);
   const isCanvasActive = useStore(INTERACTION_SCOPES.canvas.$isActive);
@@ -27,7 +27,7 @@ export const CanvasToolbarResetViewButton = memo(() => {
   }, [canvasManager]);
 
   const onReset = useCallback(() => {
-    if ($alt.get()) {
+    if ($shift.get()) {
       resetView();
     } else {
       resetZoom();
@@ -35,7 +35,7 @@ export const CanvasToolbarResetViewButton = memo(() => {
   }, [resetView, resetZoom]);
 
   useHotkeys('r', resetView, { enabled: isCanvasActive }, [isCanvasActive]);
-  useHotkeys('alt+r', resetZoom, { enabled: isCanvasActive }, [isCanvasActive]);
+  useHotkeys('shift+r', resetZoom, { enabled: isCanvasActive }, [isCanvasActive]);
 
   return (
     <IconButton
@@ -48,4 +48,4 @@ export const CanvasToolbarResetViewButton = memo(() => {
   );
 });
 
-CanvasToolbarResetViewButton.displayName = 'CanvasToolbarResetViewButton';
+CanvasResetViewButton.displayName = 'CanvasResetViewButton';

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

@@ -0,0 +1,145 @@
+import { Flex, Grid, GridItem, IconButton } from '@invoke-ai/ui-library';
+import { memo, useCallback, useState } from 'react';
+import {
+  PiArrowDownBold,
+  PiArrowDownLeftBold,
+  PiArrowDownRightBold,
+  PiArrowLeftBold,
+  PiArrowRightBold,
+  PiArrowUpBold,
+  PiArrowUpLeftBold,
+  PiArrowUpRightBold,
+  PiSquareBold,
+} from 'react-icons/pi';
+
+type ResizeDirection =
+  | 'up-left'
+  | 'up'
+  | 'up-right'
+  | 'left'
+  | 'center-out'
+  | 'right'
+  | 'down-left'
+  | 'down'
+  | 'down-right';
+
+export const CanvasResizer = memo(() => {
+  const [resizeDirection, setResizeDirection] = useState<ResizeDirection>('center-out');
+
+  const setDirUpLeft = useCallback(() => {
+    setResizeDirection('up-left');
+  }, []);
+
+  const setDirUp = useCallback(() => {
+    setResizeDirection('up');
+  }, []);
+
+  const setDirUpRight = useCallback(() => {
+    setResizeDirection('up-right');
+  }, []);
+
+  const setDirLeft = useCallback(() => {
+    setResizeDirection('left');
+  }, []);
+
+  const setDirCenterOut = useCallback(() => {
+    setResizeDirection('center-out');
+  }, []);
+
+  const setDirRight = useCallback(() => {
+    setResizeDirection('right');
+  }, []);
+
+  const setDirDownLeft = useCallback(() => {
+    setResizeDirection('down-left');
+  }, []);
+
+  const setDirDown = useCallback(() => {
+    setResizeDirection('down');
+  }, []);
+
+  const setDirDownRight = useCallback(() => {
+    setResizeDirection('down-right');
+  }, []);
+
+  return (
+    <Flex p={2}>
+      <Grid gridTemplateRows="1fr 1fr 1fr" gridTemplateColumns="1fr 1fr 1fr" gap={2}>
+        <GridItem>
+          <IconButton
+            onClick={setDirUpLeft}
+            aria-label="up-left"
+            icon={<PiArrowUpLeftBold />}
+            variant={resizeDirection === 'up-left' ? 'solid' : 'ghost'}
+          />
+        </GridItem>
+        <GridItem>
+          <IconButton
+            onClick={setDirUp}
+            aria-label="up"
+            icon={<PiArrowUpBold />}
+            variant={resizeDirection === 'up' ? 'solid' : 'ghost'}
+          />
+        </GridItem>
+        <GridItem>
+          <IconButton
+            onClick={setDirUpRight}
+            aria-label="up-right"
+            icon={<PiArrowUpRightBold />}
+            variant={resizeDirection === 'up-right' ? 'solid' : 'ghost'}
+          />
+        </GridItem>
+        <GridItem>
+          <IconButton
+            onClick={setDirLeft}
+            aria-label="left"
+            icon={<PiArrowLeftBold />}
+            variant={resizeDirection === 'left' ? 'solid' : 'ghost'}
+          />
+        </GridItem>
+        <GridItem>
+          <IconButton
+            onClick={setDirCenterOut}
+            aria-label="center-out"
+            icon={<PiSquareBold />}
+            variant={resizeDirection === 'center-out' ? 'solid' : 'ghost'}
+          />
+        </GridItem>
+        <GridItem>
+          <IconButton
+            onClick={setDirRight}
+            aria-label="right"
+            icon={<PiArrowRightBold />}
+            variant={resizeDirection === 'right' ? 'solid' : 'ghost'}
+          />
+        </GridItem>
+        <GridItem>
+          <IconButton
+            onClick={setDirDownLeft}
+            aria-label="down-left"
+            icon={<PiArrowDownLeftBold />}
+            variant={resizeDirection === 'down-left' ? 'solid' : 'ghost'}
+          />
+        </GridItem>
+        <GridItem>
+          <IconButton
+            onClick={setDirDown}
+            aria-label="down"
+            icon={<PiArrowDownBold />}
+            variant={resizeDirection === 'down' ? 'solid' : 'ghost'}
+          />
+        </GridItem>
+        <GridItem>
+          <IconButton
+            onClick={setDirDownRight}
+            aria-label="down-right"
+            icon={<PiArrowDownRightBold />}
+            variant={resizeDirection === 'down-right' ? 'solid' : 'ghost'}
+          />
+        </GridItem>
+      </Grid>
+    </Flex>
+  );
+});
+
+CanvasResizer.displayName = 'CanvasResizer';

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

@@ -15,8 +15,9 @@ import {
 } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
+import { MAX_CANVAS_SCALE, MIN_CANVAS_SCALE } from 'features/controlLayers/konva/constants';
 import { snapToNearest } from 'features/controlLayers/konva/util';
-import { round } from 'lodash-es';
+import { clamp, round } from 'lodash-es';
 import { computed } from 'nanostores';
 import type { KeyboardEvent } from 'react';
 import { memo, useCallback, useEffect, useState } from 'react';
@@ -31,7 +32,7 @@ function formatPct(v: number | string) {
   return `${round(Number(v), 2).toLocaleString()}%`;
 }
 
-function mapSliderValueToRawValue(value: number) {
+function mapSliderValueToScale(value: number) {
   if (value <= 40) {
     // 0 to 40 -> 10% to 100%
     return 10 + (90 * value) / 40;
@@ -44,58 +45,64 @@ function mapSliderValueToRawValue(value: number) {
   }
 }
 
-function mapRawValueToSliderValue(value: number) {
-  if (value <= 100) {
-    return ((value - 10) * 40) / 90;
-  } else if (value <= 500) {
-    return 40 + ((value - 100) * 30) / 400;
+function mapScaleToSliderValue(scale: number) {
+  if (scale <= 100) {
+    return ((scale - 10) * 40) / 90;
+  } else if (scale <= 500) {
+    return 40 + ((scale - 100) * 30) / 400;
   } else {
-    return 70 + ((value - 500) * 30) / 1500;
+    return 70 + ((scale - 500) * 30) / 1500;
   }
 }
 
 function formatSliderValue(value: number) {
-  return String(mapSliderValueToRawValue(value));
+  return String(mapSliderValueToScale(value));
 }
 
 const marks = [
-  mapRawValueToSliderValue(10),
-  mapRawValueToSliderValue(50),
-  mapRawValueToSliderValue(100),
-  mapRawValueToSliderValue(500),
-  mapRawValueToSliderValue(2000),
+  mapScaleToSliderValue(10),
+  mapScaleToSliderValue(50),
+  mapScaleToSliderValue(100),
+  mapScaleToSliderValue(500),
+  mapScaleToSliderValue(2000),
 ];
 
-const sliderDefaultValue = mapRawValueToSliderValue(100);
+const sliderDefaultValue = mapScaleToSliderValue(100);
 
 const snapCandidates = marks.slice(1, marks.length - 1);
 
-export const CanvasToolbarScale = memo(() => {
+export const CanvasScale = memo(() => {
   const { t } = useTranslation();
   const canvasManager = useCanvasManager();
-  const scale = useStore(computed(canvasManager.stage.$stageAttrs, (attrs) => attrs.scale));
+  const scale = useStore(computed(canvasManager.stateApi.$stageAttrs, (attrs) => attrs.scale));
   const [localScale, setLocalScale] = useState(scale * 100);
 
   const onChangeSlider = useCallback(
     (scale: number) => {
+      if (!canvasManager) {
+        return;
+      }
       let snappedScale = scale;
       // Do not snap if shift key is held
       if (!$shift.get()) {
         snappedScale = snapToNearest(scale, snapCandidates, 2);
       }
-      const mappedScale = mapSliderValueToRawValue(snappedScale);
+      const mappedScale = mapSliderValueToScale(snappedScale);
       canvasManager.stage.setScale(mappedScale / 100);
     },
     [canvasManager]
   );
 
   const onBlur = useCallback(() => {
+    if (!canvasManager) {
+      return;
+    }
     if (isNaN(Number(localScale))) {
       canvasManager.stage.setScale(1);
       setLocalScale(100);
       return;
     }
-    canvasManager.stage.setScale(localScale / 100);
+    canvasManager.stage.setScale(clamp(localScale / 100, MIN_CANVAS_SCALE, MAX_CANVAS_SCALE));
   }, [canvasManager, localScale]);
 
   const onChangeNumberInput = useCallback((valueAsString: string, valueAsNumber: number) => {
@@ -123,8 +130,8 @@ export const CanvasToolbarScale = memo(() => {
           <NumberInput
             display="flex"
             alignItems="center"
-            min={canvasManager.stage.config.MIN_SCALE * 100}
-            max={canvasManager.stage.config.MAX_SCALE * 100}
+            min={MIN_CANVAS_SCALE * 100}
+            max={MAX_CANVAS_SCALE * 100}
             value={localScale}
             onChange={onChangeNumberInput}
             onBlur={onBlur}
@@ -155,7 +162,7 @@ export const CanvasToolbarScale = memo(() => {
           <CompositeSlider
             min={0}
             max={100}
-            value={mapRawValueToSliderValue(localScale)}
+            value={mapScaleToSliderValue(localScale)}
             onChange={onChangeSlider}
             defaultValue={sliderDefaultValue}
             marks={marks}
@@ -168,4 +175,4 @@ export const CanvasToolbarScale = memo(() => {
   );
 });
 
-CanvasToolbarScale.displayName = 'CanvasToolbarScale';
+CanvasScale.displayName = 'CanvasScale';

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

@@ -1,11 +1,7 @@
 import { Flex, Text } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { IconSwitch } from 'common/components/IconSwitch';
-import {
-  selectCanvasSettingsSlice,
-  settingsSendToCanvasChanged,
-} from 'features/controlLayers/store/canvasSettingsSlice';
+import { selectIsComposing, sessionSendToCanvasChanged } from 'features/controlLayers/store/canvasSessionSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiImageBold, PiPaintBrushBold } from 'react-icons/pi';
@@ -36,22 +32,20 @@ const TooltipSendToCanvas = memo(() => {
 
 TooltipSendToCanvas.displayName = 'TooltipSendToCanvas';
 
-const selectSendToCanvas = createSelector(selectCanvasSettingsSlice, (canvasSettings) => canvasSettings.sendToCanvas);
-
 export const CanvasSendToToggle = memo(() => {
   const dispatch = useAppDispatch();
-  const sendToCanvas = useAppSelector(selectSendToCanvas);
+  const isComposing = useAppSelector(selectIsComposing);
 
   const onChange = useCallback(
     (isChecked: boolean) => {
-      dispatch(settingsSendToCanvasChanged(isChecked));
+      dispatch(sessionSendToCanvasChanged(isChecked));
     },
     [dispatch]
   );
 
   return (
     <IconSwitch
-      isChecked={sendToCanvas}
+      isChecked={isComposing}
       onChange={onChange}
       iconUnchecked={<PiImageBold />}
       tooltipUnchecked={<TooltipSendToGallery />}

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

@@ -7,7 +7,7 @@ import { CanvasEntitySettingsWrapper } from 'features/controlLayers/components/c
 import { CanvasEntityEditableTitle } from 'features/controlLayers/components/common/CanvasEntityTitleEdit';
 import { ControlLayerBadges } from 'features/controlLayers/components/ControlLayer/ControlLayerBadges';
 import { ControlLayerControlAdapter } from 'features/controlLayers/components/ControlLayer/ControlLayerControlAdapter';
-import { ControlLayerAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
+import { EntityLayerAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { EntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo, useMemo } from 'react';
@@ -21,7 +21,7 @@ export const ControlLayer = memo(({ id }: Props) => {
 
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>
-      <ControlLayerAdapterGate>
+      <EntityLayerAdapterGate>
         <CanvasEntityContainer>
           <CanvasEntityHeader>
             <CanvasEntityPreviewImage />
@@ -34,7 +34,7 @@ export const ControlLayer = memo(({ id }: Props) => {
             <ControlLayerControlAdapter />
           </CanvasEntitySettingsWrapper>
         </CanvasEntityContainer>
-      </ControlLayerAdapterGate>
+      </EntityLayerAdapterGate>
     </EntityIdentifierContext.Provider>
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/ControlLayer/ControlLayerControlAdapterModel.tsx

@@ -4,12 +4,7 @@ import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import { selectBase } from 'features/controlLayers/store/paramsSlice';
-import {
-  IMAGE_FILTERS,
-  isControlLayerEntityIdentifier,
-  isFilterType,
-  isRasterLayerEntityIdentifier,
-} from 'features/controlLayers/store/types';
+import { IMAGE_FILTERS, isFilterType } from 'features/controlLayers/store/types';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { useControlNetAndT2IAdapterModels } from 'services/api/hooks/modelsByType';
@@ -44,11 +39,6 @@ export const ControlLayerControlAdapterModel = memo(({ modelKey, onChange: onCha
 
       // Open the filter popup by setting this entity as the filtering entity
       if (!canvasManager.filter.$adapter.get()) {
-        // Can only filter raster and control layers
-        if (!isRasterLayerEntityIdentifier(entityIdentifier) && !isControlLayerEntityIdentifier(entityIdentifier)) {
-          return;
-        }
-
         // Update the filter, preferring the model's default
         if (isFilterType(modelConfig.default_settings?.preprocessor)) {
           canvasManager.filter.$config.set(
@@ -57,8 +47,7 @@ export const ControlLayerControlAdapterModel = memo(({ modelKey, onChange: onCha
         } else {
           canvasManager.filter.$config.set(IMAGE_FILTERS.canny_image_processor.buildDefaults(modelConfig.base));
         }
-
-        canvasManager.filter.startFilter(entityIdentifier);
+        canvasManager.filter.initialize(entityIdentifier);
         canvasManager.filter.previewFilter();
       }
     },

invokeai/frontend/web/src/features/controlLayers/components/ControlLayer/ControlLayerMenuItemsControlToRaster.tsx

@@ -1,7 +1,6 @@
 import { MenuItem } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import { controlLayerConvertedToRasterLayer } from 'features/controlLayers/store/canvasSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -10,7 +9,6 @@ import { PiLightningBold } from 'react-icons/pi';
 export const ControlLayerMenuItemsControlToRaster = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
-  const isBusy = useCanvasIsBusy();
   const entityIdentifier = useEntityIdentifierContext('control_layer');
 
   const convertControlLayerToRasterLayer = useCallback(() => {
@@ -18,7 +16,7 @@ export const ControlLayerMenuItemsControlToRaster = memo(() => {
   }, [dispatch, entityIdentifier]);
 
   return (
-    <MenuItem onClick={convertControlLayerToRasterLayer} icon={<PiLightningBold />} isDisabled={isBusy}>
+    <MenuItem onClick={convertControlLayerToRasterLayer} icon={<PiLightningBold />}>
       {t('controlLayers.convertToRasterLayer')}
     </MenuItem>
   );

invokeai/frontend/web/src/features/controlLayers/components/ControlLayersEditor.stories.tsx

@@ -1,6 +1,6 @@
 import { Flex } from '@invoke-ai/ui-library';
 import type { Meta, StoryObj } from '@storybook/react';
-import { CanvasEditor } from 'features/controlLayers/components/CanvasEditor';
+import { CanvasEditor } from 'features/controlLayers/components/ControlLayersEditor';
 
 const meta: Meta<typeof CanvasEditor> = {
   title: 'Feature/ControlLayers',

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

@@ -2,12 +2,12 @@
 import { Flex } from '@invoke-ai/ui-library';
 import { useScopeOnFocus } from 'common/hooks/interactionScopes';
 import { CanvasDropArea } from 'features/controlLayers/components/CanvasDropArea';
+import { ControlLayersToolbar } from 'features/controlLayers/components/ControlLayersToolbar';
 import { Filter } from 'features/controlLayers/components/Filters/Filter';
 import { StageComponent } from 'features/controlLayers/components/StageComponent';
 import { StagingAreaIsStagingGate } from 'features/controlLayers/components/StagingArea/StagingAreaIsStagingGate';
 import { StagingAreaToolbar } from 'features/controlLayers/components/StagingArea/StagingAreaToolbar';
-import { CanvasToolbar } from 'features/controlLayers/components/Toolbar/CanvasToolbar';
-import { Transform } from 'features/controlLayers/components/Transform/Transform';
+import { Transform } from 'features/controlLayers/components/Transform';
 import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { memo, useRef } from 'react';
 
@@ -28,16 +28,16 @@ export const CanvasEditor = memo(() => {
       alignItems="center"
       justifyContent="center"
     >
-      <CanvasToolbar />
+      <ControlLayersToolbar />
       <StageComponent />
-      <Flex position="absolute" bottom={4} gap={2} align="center" justify="center">
+      <Flex position="absolute" bottom={8} gap={2} align="center" justify="center">
         <CanvasManagerProviderGate>
           <StagingAreaIsStagingGate>
             <StagingAreaToolbar />
           </StagingAreaIsStagingGate>
         </CanvasManagerProviderGate>
       </Flex>
-      <Flex position="absolute" bottom={4}>
+      <Flex position="absolute" bottom={8}>
         <CanvasManagerProviderGate>
           <Filter />
           <Transform />

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

@@ -0,0 +1,39 @@
+/* eslint-disable i18next/no-literal-string */
+import { Flex, Spacer } from '@invoke-ai/ui-library';
+import { CanvasResetViewButton } from 'features/controlLayers/components/CanvasResetViewButton';
+import { CanvasScale } from 'features/controlLayers/components/CanvasScale';
+import { SaveToGalleryButton } from 'features/controlLayers/components/SaveToGalleryButton';
+import { CanvasSettingsPopover } from 'features/controlLayers/components/Settings/CanvasSettingsPopover';
+import { ToolChooser } from 'features/controlLayers/components/Tool/ToolChooser';
+import { ToolFillColorPicker } from 'features/controlLayers/components/Tool/ToolFillColorPicker';
+import { ToolSettings } from 'features/controlLayers/components/Tool/ToolSettings';
+import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
+import { useCanvasUndoRedo } from 'features/controlLayers/hooks/useCanvasUndoRedo';
+import { ToggleProgressButton } from 'features/gallery/components/ImageViewer/ToggleProgressButton';
+import { ViewerToggle } from 'features/gallery/components/ImageViewer/ViewerToggleMenu';
+import { memo } from 'react';
+
+export const ControlLayersToolbar = memo(() => {
+  useCanvasUndoRedo();
+
+  return (
+    <CanvasManagerProviderGate>
+      <Flex w="full" gap={2} alignItems="center">
+        <ToggleProgressButton />
+        <ToolChooser />
+        <Spacer />
+        <ToolSettings />
+        <Spacer />
+        <CanvasScale />
+        <CanvasResetViewButton />
+        <Spacer />
+        <ToolFillColorPicker />
+        <SaveToGalleryButton />
+        <CanvasSettingsPopover />
+        <ViewerToggle />
+      </Flex>
+    </CanvasManagerProviderGate>
+  );
+});
+
+ControlLayersToolbar.displayName = 'ControlLayersToolbar';

invokeai/frontend/web/src/features/controlLayers/components/Filters/Filter.tsx

@@ -1,4 +1,4 @@
-import { Button, ButtonGroup, Flex, Heading, Spacer } from '@invoke-ai/ui-library';
+import { Button, ButtonGroup, Flex, Heading } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { FilterSettings } from 'features/controlLayers/components/Filters/FilterSettings';
 import { FilterTypeSelect } from 'features/controlLayers/components/Filters/FilterTypeSelect';
@@ -51,7 +51,7 @@ export const Filter = memo(() => {
       </Heading>
       <FilterTypeSelect filterType={config.type} onChange={onChangeFilterType} />
       <FilterSettings filterConfig={config} onChange={onChangeFilterConfig} />
-      <ButtonGroup isAttached={false} size="sm" w="full">
+      <ButtonGroup isAttached={false} size="sm" alignSelf="self-end">
         <Button
           variant="ghost"
           leftIcon={<PiShootingStarBold />}
@@ -61,7 +61,6 @@ export const Filter = memo(() => {
         >
           {t('controlLayers.filter.preview')}
         </Button>
-        <Spacer />
         <Button
           variant="ghost"
           leftIcon={<PiCheckBold />}

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

@@ -9,7 +9,7 @@ import { bboxHeightChanged, bboxWidthChanged } from 'features/controlLayers/stor
 import { selectOptimalDimension } from 'features/controlLayers/store/selectors';
 import type { ImageWithDims } from 'features/controlLayers/store/types';
 import type { ImageDraggableData, TypesafeDroppableData } from 'features/dnd/types';
-import { calculateNewSize } from 'features/parameters/components/Bbox/calculateNewSize';
+import { calculateNewSize } from 'features/parameters/components/DocumentSize/calculateNewSize';
 import { memo, useCallback, useEffect, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowCounterClockwiseBold, PiRulerBold } from 'react-icons/pi';

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

@@ -4,7 +4,7 @@ import { CanvasEntityHeader } from 'features/controlLayers/components/common/Can
 import { CanvasEntityHeaderCommonActions } from 'features/controlLayers/components/common/CanvasEntityHeaderCommonActions';
 import { CanvasEntityPreviewImage } from 'features/controlLayers/components/common/CanvasEntityPreviewImage';
 import { CanvasEntityEditableTitle } from 'features/controlLayers/components/common/CanvasEntityTitleEdit';
-import { InpaintMaskAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
+import { EntityMaskAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { EntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo, useMemo } from 'react';
@@ -18,7 +18,7 @@ export const InpaintMask = memo(({ id }: Props) => {
 
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>
-      <InpaintMaskAdapterGate>
+      <EntityMaskAdapterGate>
         <CanvasEntityContainer>
           <CanvasEntityHeader>
             <CanvasEntityPreviewImage />
@@ -27,7 +27,7 @@ export const InpaintMask = memo(({ id }: Props) => {
             <CanvasEntityHeaderCommonActions />
           </CanvasEntityHeader>
         </CanvasEntityContainer>
-      </InpaintMaskAdapterGate>
+      </EntityMaskAdapterGate>
     </EntityIdentifierContext.Provider>
   );
 });

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

@@ -4,7 +4,7 @@ import { CanvasEntityHeader } from 'features/controlLayers/components/common/Can
 import { CanvasEntityHeaderCommonActions } from 'features/controlLayers/components/common/CanvasEntityHeaderCommonActions';
 import { CanvasEntityPreviewImage } from 'features/controlLayers/components/common/CanvasEntityPreviewImage';
 import { CanvasEntityEditableTitle } from 'features/controlLayers/components/common/CanvasEntityTitleEdit';
-import { RasterLayerAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
+import { EntityLayerAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { EntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo, useMemo } from 'react';
@@ -18,7 +18,7 @@ export const RasterLayer = memo(({ id }: Props) => {
 
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>
-      <RasterLayerAdapterGate>
+      <EntityLayerAdapterGate>
         <CanvasEntityContainer>
           <CanvasEntityHeader>
             <CanvasEntityPreviewImage />
@@ -27,7 +27,7 @@ export const RasterLayer = memo(({ id }: Props) => {
             <CanvasEntityHeaderCommonActions />
           </CanvasEntityHeader>
         </CanvasEntityContainer>
-      </RasterLayerAdapterGate>
+      </EntityLayerAdapterGate>
     </EntityIdentifierContext.Provider>
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/RasterLayer/RasterLayerMenuItemsRasterToControl.tsx

@@ -1,7 +1,6 @@
 import { MenuItem } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import { rasterLayerConvertedToControlLayer } from 'features/controlLayers/store/canvasSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -11,14 +10,13 @@ export const RasterLayerMenuItemsRasterToControl = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const entityIdentifier = useEntityIdentifierContext('raster_layer');
-  const isBusy = useCanvasIsBusy();
 
   const convertRasterLayerToControlLayer = useCallback(() => {
     dispatch(rasterLayerConvertedToControlLayer({ entityIdentifier }));
   }, [dispatch, entityIdentifier]);
 
   return (
-    <MenuItem onClick={convertRasterLayerToControlLayer} icon={<PiLightningBold />} isDisabled={isBusy}>
+    <MenuItem onClick={convertRasterLayerToControlLayer} icon={<PiLightningBold />}>
       {t('controlLayers.convertToControlLayer')}
     </MenuItem>
   );

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

@@ -6,7 +6,7 @@ import { CanvasEntityPreviewImage } from 'features/controlLayers/components/comm
 import { CanvasEntityEditableTitle } from 'features/controlLayers/components/common/CanvasEntityTitleEdit';
 import { RegionalGuidanceBadges } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceBadges';
 import { RegionalGuidanceSettings } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceSettings';
-import { RegionalGuidanceAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
+import { EntityMaskAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { EntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo, useMemo } from 'react';
@@ -20,7 +20,7 @@ export const RegionalGuidance = memo(({ id }: Props) => {
 
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>
-      <RegionalGuidanceAdapterGate>
+      <EntityMaskAdapterGate>
         <CanvasEntityContainer>
           <CanvasEntityHeader>
             <CanvasEntityPreviewImage />
@@ -31,7 +31,7 @@ export const RegionalGuidance = memo(({ id }: Props) => {
           </CanvasEntityHeader>
           <RegionalGuidanceSettings />
         </CanvasEntityContainer>
-      </RegionalGuidanceAdapterGate>
+      </EntityMaskAdapterGate>
     </EntityIdentifierContext.Provider>
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/RegionalGuidance/RegionalGuidanceMenuItemsAddPromptsAndIPAdapter.tsx

@@ -2,7 +2,6 @@ import { MenuItem } from '@invoke-ai/ui-library';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import {
   rgIPAdapterAdded,
   rgNegativePromptChanged,
@@ -16,7 +15,6 @@ export const RegionalGuidanceMenuItemsAddPromptsAndIPAdapter = memo(() => {
   const entityIdentifier = useEntityIdentifierContext('regional_guidance');
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
-  const isBusy = useCanvasIsBusy();
   const selectValidActions = useMemo(
     () =>
       createMemoizedSelector(selectCanvasSlice, (canvas) => {
@@ -41,15 +39,13 @@ export const RegionalGuidanceMenuItemsAddPromptsAndIPAdapter = memo(() => {
 
   return (
     <>
-      <MenuItem onClick={addPositivePrompt} isDisabled={!validActions.canAddPositivePrompt || isBusy}>
+      <MenuItem onClick={addPositivePrompt} isDisabled={!validActions.canAddPositivePrompt}>
         {t('controlLayers.addPositivePrompt')}
       </MenuItem>
-      <MenuItem onClick={addNegativePrompt} isDisabled={!validActions.canAddNegativePrompt || isBusy}>
+      <MenuItem onClick={addNegativePrompt} isDisabled={!validActions.canAddNegativePrompt}>
         {t('controlLayers.addNegativePrompt')}
       </MenuItem>
-      <MenuItem onClick={addIPAdapter} isDisabled={isBusy}>
-        {t('controlLayers.addIPAdapter')}
-      </MenuItem>
+      <MenuItem onClick={addIPAdapter}>{t('controlLayers.addIPAdapter')}</MenuItem>
     </>
   );
 });

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

@@ -13,7 +13,7 @@ const log = logger('canvas');
 
 const [useIsSaving] = buildUseBoolean(false);
 
-export const CanvasToolbarSaveToGalleryButton = memo(() => {
+export const SaveToGalleryButton = memo(() => {
   const { t } = useTranslation();
   const shift = useShiftModifier();
   const canvasManager = useCanvasManager();
@@ -50,4 +50,4 @@ export const CanvasToolbarSaveToGalleryButton = memo(() => {
   );
 });
 
-CanvasToolbarSaveToGalleryButton.displayName = 'CanvasToolbarSaveToGalleryButton';
+SaveToGalleryButton.displayName = 'SaveToGalleryButton';

invokeai/frontend/web/src/features/controlLayers/components/Settings/CanvasSettingsClipToBboxCheckbox.tsx

@@ -1,7 +1,7 @@
 import { Checkbox, FormControl, FormLabel } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { selectCanvasSettingsSlice, settingsClipToBboxChanged } from 'features/controlLayers/store/canvasSettingsSlice';
+import { clipToBboxChanged, selectCanvasSettingsSlice } from 'features/controlLayers/store/canvasSettingsSlice';
 import type { ChangeEvent } from 'react';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -13,7 +13,7 @@ export const CanvasSettingsClipToBboxCheckbox = memo(() => {
   const dispatch = useAppDispatch();
   const clipToBbox = useAppSelector(selectClipToBbox);
   const onChange = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => dispatch(settingsClipToBboxChanged(e.target.checked)),
+    (e: ChangeEvent<HTMLInputElement>) => dispatch(clipToBboxChanged(e.target.checked)),
     [dispatch]
   );
   return (

invokeai/frontend/web/src/features/controlLayers/components/Settings/CanvasSettingsCompositeMaskedRegionsCheckbox.tsx

@@ -1,33 +0,0 @@
-import { Checkbox, FormControl, FormLabel } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import {
-  selectCanvasSettingsSlice,
-  settingsCompositeMaskedRegionsChanged,
-} from 'features/controlLayers/store/canvasSettingsSlice';
-import type { ChangeEvent } from 'react';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-const selectCompositeMaskedRegions = createSelector(
-  selectCanvasSettingsSlice,
-  (canvasSettings) => canvasSettings.compositeMaskedRegions
-);
-
-export const CanvasSettingsCompositeMaskedRegionsCheckbox = memo(() => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const compositeMaskedRegions = useAppSelector(selectCompositeMaskedRegions);
-  const onChange = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => dispatch(settingsCompositeMaskedRegionsChanged(e.target.checked)),
-    [dispatch]
-  );
-  return (
-    <FormControl w="full">
-      <FormLabel flexGrow={1}>{t('controlLayers.compositeMaskedRegions')}</FormLabel>
-      <Checkbox isChecked={compositeMaskedRegions} onChange={onChange} />
-    </FormControl>
-  );
-});
-
-CanvasSettingsCompositeMaskedRegionsCheckbox.displayName = 'CanvasSettingsCompositeMaskedRegionsCheckbox';

invokeai/frontend/web/src/features/controlLayers/components/Settings/CanvasSettingsDynamicGridSwitch.tsx

@@ -1,9 +1,15 @@
 import { FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
+import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { selectDynamicGrid, settingsDynamicGridToggled } from 'features/controlLayers/store/canvasSettingsSlice';
+import {
+  selectCanvasSettingsSlice,
+  settingsDynamicGridToggled,
+} from 'features/controlLayers/store/canvasSettingsSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
+const selectDynamicGrid = createSelector(selectCanvasSettingsSlice, (canvasSettings) => canvasSettings.dynamicGrid);
+
 export const CanvasSettingsDynamicGridSwitch = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();

invokeai/frontend/web/src/features/controlLayers/components/Settings/CanvasSettingsInvertScrollCheckbox.tsx

@@ -1,33 +1,25 @@
 import { Checkbox, FormControl, FormLabel } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import {
-  selectCanvasSettingsSlice,
-  settingsInvertScrollForToolWidthChanged,
-} from 'features/controlLayers/store/canvasSettingsSlice';
+import { invertScrollChanged, selectToolSlice } from 'features/controlLayers/store/toolSlice';
 import type { ChangeEvent } from 'react';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-const selectInvertScrollForToolWidth = createSelector(
-  selectCanvasSettingsSlice,
-  (settings) => settings.invertScrollForToolWidth
-);
+const selectInvertScroll = createSelector(selectToolSlice, (tool) => tool.invertScroll);
 
 export const CanvasSettingsInvertScrollCheckbox = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
-  const invertScrollForToolWidth = useAppSelector(selectInvertScrollForToolWidth);
+  const invertScroll = useAppSelector(selectInvertScroll);
   const onChange = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => {
-      dispatch(settingsInvertScrollForToolWidthChanged(e.target.checked));
-    },
+    (e: ChangeEvent<HTMLInputElement>) => dispatch(invertScrollChanged(e.target.checked)),
     [dispatch]
   );
   return (
     <FormControl w="full">
       <FormLabel flexGrow={1}>{t('unifiedCanvas.invertBrushSizeScrollDirection')}</FormLabel>
-      <Checkbox isChecked={invertScrollForToolWidth} onChange={onChange} />
+      <Checkbox isChecked={invertScroll} onChange={onChange} />
     </FormControl>
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/Settings/CanvasSettingsPopover.tsx

@@ -13,7 +13,6 @@ import { CanvasSettingsAutoSaveCheckbox } from 'features/controlLayers/component
 import { CanvasSettingsClearCachesButton } from 'features/controlLayers/components/Settings/CanvasSettingsClearCachesButton';
 import { CanvasSettingsClearHistoryButton } from 'features/controlLayers/components/Settings/CanvasSettingsClearHistoryButton';
 import { CanvasSettingsClipToBboxCheckbox } from 'features/controlLayers/components/Settings/CanvasSettingsClipToBboxCheckbox';
-import { CanvasSettingsCompositeMaskedRegionsCheckbox } from 'features/controlLayers/components/Settings/CanvasSettingsCompositeMaskedRegionsCheckbox';
 import { CanvasSettingsDynamicGridSwitch } from 'features/controlLayers/components/Settings/CanvasSettingsDynamicGridSwitch';
 import { CanvasSettingsInvertScrollCheckbox } from 'features/controlLayers/components/Settings/CanvasSettingsInvertScrollCheckbox';
 import { CanvasSettingsLogDebugInfoButton } from 'features/controlLayers/components/Settings/CanvasSettingsLogDebugInfo';
@@ -38,7 +37,6 @@ export const CanvasSettingsPopover = memo(() => {
             <CanvasSettingsAutoSaveCheckbox />
             <CanvasSettingsInvertScrollCheckbox />
             <CanvasSettingsClipToBboxCheckbox />
-            <CanvasSettingsCompositeMaskedRegionsCheckbox />
             <CanvasSettingsDynamicGridSwitch />
             <CanvasSettingsShowHUDSwitch />
             <CanvasSettingsResetButton />

invokeai/frontend/web/src/features/controlLayers/components/Settings/CanvasSettingsRecalculateRectsButton.tsx

@@ -7,7 +7,7 @@ export const CanvasSettingsRecalculateRectsButton = memo(() => {
   const { t } = useTranslation();
   const canvasManager = useCanvasManager();
   const onClick = useCallback(() => {
-    for (const adapter of canvasManager.getAllAdapters()) {
+    for (const adapter of canvasManager.adapters.getAll()) {
       adapter.transformer.requestRectCalculation();
     }
   }, [canvasManager]);

invokeai/frontend/web/src/features/controlLayers/components/Settings/CanvasSettingsResetButton.tsx

@@ -1,6 +1,5 @@
 import { Button } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { canvasReset } from 'features/controlLayers/store/canvasSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -8,11 +7,9 @@ import { useTranslation } from 'react-i18next';
 export const CanvasSettingsResetButton = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
-  const canvasManager = useCanvasManager();
   const onClick = useCallback(() => {
     dispatch(canvasReset());
-    canvasManager.stage.fitLayersToStage();
-  }, [canvasManager.stage, dispatch]);
+  }, [dispatch]);
   return (
     <Button onClick={onClick} colorScheme="error" size="sm">
       {t('controlLayers.resetCanvas')}

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

@@ -1,14 +1,15 @@
 import { Flex } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
+import { createSelector } from '@reduxjs/toolkit';
 import { $socket } from 'app/hooks/useSocketIO';
 import { logger } from 'app/logging/logger';
 import { useAppStore } from 'app/store/nanostores/store';
 import { useAppSelector } from 'app/store/storeHooks';
 import { HeadsUpDisplay } from 'features/controlLayers/components/HeadsUpDisplay';
 import { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
-import { TRANSPARENCY_CHECKERBOARD_PATTERN_DATAURL } from 'features/controlLayers/konva/patterns/transparency-checkerboard-pattern';
+import { TRANSPARENCY_CHECKER_PATTERN } from 'features/controlLayers/konva/constants';
 import { getPrefixedId } from 'features/controlLayers/konva/util';
-import { selectDynamicGrid, selectShowHUD } from 'features/controlLayers/store/canvasSettingsSlice';
+import { selectCanvasSettingsSlice } from 'features/controlLayers/store/canvasSettingsSlice';
 import Konva from 'konva';
 import { memo, useCallback, useEffect, useLayoutEffect, useState } from 'react';
 import { useDevicePixelRatio } from 'use-device-pixel-ratio';
@@ -46,6 +47,9 @@ const useStageRenderer = (stage: Konva.Stage, container: HTMLDivElement | null)
   }, [dpr]);
 };
 
+const selectDynamicGrid = createSelector(selectCanvasSettingsSlice, (canvasSettings) => canvasSettings.dynamicGrid);
+const selectShowHUD = createSelector(selectCanvasSettingsSlice, (canvasSettings) => canvasSettings.showHUD);
+
 export const StageComponent = memo(() => {
   const dynamicGrid = useAppSelector(selectDynamicGrid);
   const showHUD = useAppSelector(selectShowHUD);
@@ -78,7 +82,7 @@ export const StageComponent = memo(() => {
         <Flex
           position="absolute"
           borderRadius="base"
-          bgImage={TRANSPARENCY_CHECKERBOARD_PATTERN_DATAURL}
+          bgImage={TRANSPARENCY_CHECKER_PATTERN}
           top={0}
           right={0}
           bottom={0}

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbar.tsx

@@ -45,7 +45,7 @@ export const StagingAreaToolbar = memo(() => {
   const index = useAppSelector(selectStagedImageIndex);
   const selectedImage = useAppSelector(selectSelectedImage);
   const imageCount = useAppSelector(selectImageCount);
-  const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
+  const shouldShowStagedImage = useStore(canvasManager.stateApi.$shouldShowStagedImage);
   const isCanvasActive = useStore(INTERACTION_SCOPES.canvas.$isActive);
   const [changeIsImageIntermediate] = useChangeImageIsIntermediateMutation();
   useScopeOnMount('stagingArea');
@@ -83,8 +83,8 @@ export const StagingAreaToolbar = memo(() => {
   }, [dispatch]);
 
   const onToggleShouldShowStagedImage = useCallback(() => {
-    canvasManager.stagingArea.$shouldShowStagedImage.set(!shouldShowStagedImage);
-  }, [canvasManager.stagingArea.$shouldShowStagedImage, shouldShowStagedImage]);
+    canvasManager.stateApi.$shouldShowStagedImage.set(!shouldShowStagedImage);
+  }, [canvasManager.stateApi.$shouldShowStagedImage, shouldShowStagedImage]);
 
   const onSaveStagingImage = useCallback(() => {
     if (!selectedImage) {

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolBboxButton.tsx

@@ -1,6 +1,10 @@
 import { IconButton } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useSelectTool, useToolIsSelected } from 'features/controlLayers/components/Tool/hooks';
-import { memo } from 'react';
+import { useIsFiltering } from 'features/controlLayers/hooks/useIsFiltering';
+import { useIsTransforming } from 'features/controlLayers/hooks/useIsTransforming';
+import { selectIsStaging } from 'features/controlLayers/store/canvasSessionSlice';
+import { memo, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiBoundingBoxBold } from 'react-icons/pi';
@@ -9,18 +13,24 @@ export const ToolBboxButton = memo(() => {
   const { t } = useTranslation();
   const selectBbox = useSelectTool('bbox');
   const isSelected = useToolIsSelected('bbox');
+  const isFiltering = useIsFiltering();
+  const isTransforming = useIsTransforming();
+  const isStaging = useAppSelector(selectIsStaging);
+  const isDisabled = useMemo(() => {
+    return isTransforming || isFiltering || isStaging;
+  }, [isFiltering, isStaging, isTransforming]);
 
-  useHotkeys('c', selectBbox, { enabled: !isSelected }, [selectBbox, isSelected]);
+  useHotkeys('q', selectBbox, { enabled: !isDisabled || isSelected }, [selectBbox, isSelected, isDisabled]);
 
   return (
     <IconButton
-      aria-label={`${t('controlLayers.tool.bbox')} (C)`}
-      tooltip={`${t('controlLayers.tool.bbox')} (C)`}
+      aria-label={`${t('controlLayers.tool.bbox')} (Q)`}
+      tooltip={`${t('controlLayers.tool.bbox')} (Q)`}
       icon={<PiBoundingBoxBold />}
       colorScheme={isSelected ? 'invokeBlue' : 'base'}
-      variant="solid"
+      variant="outline"
       onClick={selectBbox}
-      isDisabled={isSelected}
+      isDisabled={isDisabled}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolBrushButton.tsx

@@ -1,16 +1,29 @@
 import { IconButton } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useSelectTool, useToolIsSelected } from 'features/controlLayers/components/Tool/hooks';
-import { memo } from 'react';
+import { useIsFiltering } from 'features/controlLayers/hooks/useIsFiltering';
+import { useIsTransforming } from 'features/controlLayers/hooks/useIsTransforming';
+import { selectIsStaging } from 'features/controlLayers/store/canvasSessionSlice';
+import { selectIsSelectedEntityDrawable } from 'features/controlLayers/store/selectors';
+import { memo, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiPaintBrushBold } from 'react-icons/pi';
 
 export const ToolBrushButton = memo(() => {
   const { t } = useTranslation();
-  const isSelected = useToolIsSelected('brush');
+  const isFiltering = useIsFiltering();
+  const isTransforming = useIsTransforming();
+  const isStaging = useAppSelector(selectIsStaging);
   const selectBrush = useSelectTool('brush');
+  const isSelected = useToolIsSelected('brush');
+  const isDrawingToolAllowed = useAppSelector(selectIsSelectedEntityDrawable);
 
-  useHotkeys('b', selectBrush, { enabled: !isSelected }, [isSelected, selectBrush]);
+  const isDisabled = useMemo(() => {
+    return isTransforming || isFiltering || isStaging || !isDrawingToolAllowed;
+  }, [isDrawingToolAllowed, isFiltering, isStaging, isTransforming]);
+
+  useHotkeys('b', selectBrush, { enabled: !isDisabled || isSelected }, [isDisabled, isSelected, selectBrush]);
 
   return (
     <IconButton
@@ -18,9 +31,9 @@ export const ToolBrushButton = memo(() => {
       tooltip={`${t('controlLayers.tool.brush')} (B)`}
       icon={<PiPaintBrushBold />}
       colorScheme={isSelected ? 'invokeBlue' : 'base'}
-      variant="solid"
+      variant="outline"
       onClick={selectBrush}
-      isDisabled={isSelected}
+      isDisabled={isDisabled}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolBrushWidth.tsx

@@ -1,12 +1,9 @@
 import {
+  CompositeNumberInput,
   CompositeSlider,
   FormControl,
   FormLabel,
-  IconButton,
-  NumberInput,
-  NumberInputField,
   Popover,
-  PopoverAnchor,
   PopoverArrow,
   PopoverBody,
   PopoverContent,
@@ -14,172 +11,47 @@ import {
 } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { useToolIsSelected } from 'features/controlLayers/components/Tool/hooks';
-import { selectCanvasSettingsSlice, settingsBrushWidthChanged } from 'features/controlLayers/store/canvasSettingsSlice';
-import { clamp } from 'lodash-es';
-import type { KeyboardEvent } from 'react';
-import { memo, useCallback, useEffect, useState } from 'react';
-import { useHotkeys } from 'react-hotkeys-hook';
+import { brushWidthChanged, selectToolSlice } from 'features/controlLayers/store/toolSlice';
+import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiCaretDownBold } from 'react-icons/pi';
 
-const selectBrushWidth = createSelector(selectCanvasSettingsSlice, (settings) => settings.brushWidth);
+const marks = [0, 100, 200, 300];
 const formatPx = (v: number | string) => `${v} px`;
-
-function mapSliderValueToRawValue(value: number) {
-  if (value <= 40) {
-    // 0 to 40 on the slider -> 1px to 50px
-    return 1 + (49 * value) / 40;
-  } else if (value <= 70) {
-    // 40 to 70 on the slider -> 50px to 200px
-    return 50 + (150 * (value - 40)) / 30;
-  } else {
-    // 70 to 100 on the slider -> 200px to 600px
-    return 200 + (400 * (value - 70)) / 30;
-  }
-}
-
-function mapRawValueToSliderValue(value: number) {
-  if (value <= 50) {
-    // 1px to 50px -> 0 to 40 on the slider
-    return ((value - 1) * 40) / 49;
-  } else if (value <= 200) {
-    // 50px to 200px -> 40 to 70 on the slider
-    return 40 + ((value - 50) * 30) / 150;
-  } else {
-    // 200px to 600px -> 70 to 100 on the slider
-    return 70 + ((value - 200) * 30) / 400;
-  }
-}
-
-function formatSliderValue(value: number) {
-  return `${String(mapSliderValueToRawValue(value))} px`;
-}
-
-const marks = [
-  mapRawValueToSliderValue(1),
-  mapRawValueToSliderValue(50),
-  mapRawValueToSliderValue(200),
-  mapRawValueToSliderValue(600),
-];
-
-const sliderDefaultValue = mapRawValueToSliderValue(50);
+const selectBrushWidth = createSelector(selectToolSlice, (tool) => tool.brush.width);
 
 export const ToolBrushWidth = memo(() => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
-  const isSelected = useToolIsSelected('brush');
   const width = useAppSelector(selectBrushWidth);
-  const [localValue, setLocalValue] = useState(width);
   const onChange = useCallback(
     (v: number) => {
-      dispatch(settingsBrushWidthChanged(clamp(Math.round(v), 1, 600)));
+      dispatch(brushWidthChanged(Math.round(v)));
     },
     [dispatch]
   );
-
-  const increment = useCallback(() => {
-    let newWidth = Math.round(width * 1.15);
-    if (newWidth === width) {
-      newWidth += 1;
-    }
-    onChange(newWidth);
-  }, [onChange, width]);
-
-  const decrement = useCallback(() => {
-    let newWidth = Math.round(width * 0.85);
-    if (newWidth === width) {
-      newWidth -= 1;
-    }
-    onChange(newWidth);
-  }, [onChange, width]);
-
-  const onChangeSlider = useCallback(
-    (value: number) => {
-      onChange(mapSliderValueToRawValue(value));
-    },
-    [onChange]
-  );
-
-  const onBlur = useCallback(() => {
-    if (isNaN(Number(localValue))) {
-      onChange(50);
-      setLocalValue(50);
-    } else {
-      onChange(localValue);
-    }
-  }, [localValue, onChange]);
-
-  const onChangeNumberInput = useCallback((valueAsString: string, valueAsNumber: number) => {
-    setLocalValue(valueAsNumber);
-  }, []);
-
-  const onKeyDown = useCallback(
-    (e: KeyboardEvent<HTMLInputElement>) => {
-      if (e.key === 'Enter') {
-        onBlur();
-      }
-    },
-    [onBlur]
-  );
-
-  useEffect(() => {
-    setLocalValue(width);
-  }, [width]);
-
-  useHotkeys('[', decrement, { enabled: isSelected }, [decrement, isSelected]);
-  useHotkeys(']', increment, { enabled: isSelected }, [increment, isSelected]);
-
   return (
-    <Popover>
-      <FormControl w="min-content" gap={2}>
-        <FormLabel m={0}>{t('controlLayers.width')}</FormLabel>
-        <PopoverAnchor>
-          <NumberInput
-            display="flex"
-            alignItems="center"
+    <FormControl w="min-content" gap={2}>
+      <FormLabel m={0}>{t('controlLayers.width')}</FormLabel>
+      <Popover isLazy>
+        <PopoverTrigger>
+          <CompositeNumberInput
             min={1}
             max={600}
-            value={localValue}
-            onChange={onChangeNumberInput}
-            onBlur={onBlur}
-            w="76px"
-            format={formatPx}
             defaultValue={50}
-            onKeyDown={onKeyDown}
-            clampValueOnBlur={false}
-          >
-            <NumberInputField paddingInlineEnd={7} />
-            <PopoverTrigger>
-              <IconButton
-                aria-label="open-slider"
-                icon={<PiCaretDownBold />}
-                size="sm"
-                variant="link"
-                position="absolute"
-                insetInlineEnd={0}
-                h="full"
-              />
-            </PopoverTrigger>
-          </NumberInput>
-        </PopoverAnchor>
-      </FormControl>
-      <PopoverContent w={200} pt={0} pb={2} px={4}>
-        <PopoverArrow />
-        <PopoverBody>
-          <CompositeSlider
-            min={0}
-            max={100}
-            value={mapRawValueToSliderValue(localValue)}
-            onChange={onChangeSlider}
-            defaultValue={sliderDefaultValue}
-            marks={marks}
-            formatValue={formatSliderValue}
-            alwaysShowMarks
+            value={width}
+            onChange={onChange}
+            w={24}
+            format={formatPx}
           />
-        </PopoverBody>
-      </PopoverContent>
-    </Popover>
+        </PopoverTrigger>
+        <PopoverContent w={200} py={2} px={4}>
+          <PopoverArrow />
+          <PopoverBody>
+            <CompositeSlider min={1} max={300} defaultValue={50} value={width} onChange={onChange} marks={marks} />
+          </PopoverBody>
+        </PopoverContent>
+      </Popover>
+    </FormControl>
   );
 });
 

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

@@ -4,11 +4,16 @@ import { ToolBrushButton } from 'features/controlLayers/components/Tool/ToolBrus
 import { ToolColorPickerButton } from 'features/controlLayers/components/Tool/ToolColorPickerButton';
 import { ToolMoveButton } from 'features/controlLayers/components/Tool/ToolMoveButton';
 import { ToolRectButton } from 'features/controlLayers/components/Tool/ToolRectButton';
+import { useCanvasDeleteLayerHotkey } from 'features/controlLayers/hooks/useCanvasDeleteLayerHotkey';
+import { useCanvasResetLayerHotkey } from 'features/controlLayers/hooks/useCanvasResetLayerHotkey';
 
 import { ToolEraserButton } from './ToolEraserButton';
 import { ToolViewButton } from './ToolViewButton';
 
 export const ToolChooser: React.FC = () => {
+  useCanvasResetLayerHotkey();
+  useCanvasDeleteLayerHotkey();
+
   return (
     <>
       <ButtonGroup isAttached>

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolColorPickerButton.tsx

@@ -1,16 +1,31 @@
 import { IconButton } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useSelectTool, useToolIsSelected } from 'features/controlLayers/components/Tool/hooks';
-import { memo } from 'react';
+import { useIsFiltering } from 'features/controlLayers/hooks/useIsFiltering';
+import { useIsTransforming } from 'features/controlLayers/hooks/useIsTransforming';
+import { selectIsStaging } from 'features/controlLayers/store/canvasSessionSlice';
+import { memo, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiEyedropperBold } from 'react-icons/pi';
 
 export const ToolColorPickerButton = memo(() => {
   const { t } = useTranslation();
-  const isSelected = useToolIsSelected('colorPicker');
+  const isFiltering = useIsFiltering();
+  const isTransforming = useIsTransforming();
   const selectColorPicker = useSelectTool('colorPicker');
+  const isSelected = useToolIsSelected('colorPicker');
+  const isStaging = useAppSelector(selectIsStaging);
 
-  useHotkeys('i', selectColorPicker, { enabled: !isSelected }, [selectColorPicker, isSelected]);
+  const isDisabled = useMemo(() => {
+    return isTransforming || isFiltering || isStaging;
+  }, [isFiltering, isStaging, isTransforming]);
+
+  useHotkeys('i', selectColorPicker, { enabled: !isDisabled || isSelected }, [
+    selectColorPicker,
+    isSelected,
+    isDisabled,
+  ]);
 
   return (
     <IconButton
@@ -18,9 +33,9 @@ export const ToolColorPickerButton = memo(() => {
       tooltip={`${t('controlLayers.tool.colorPicker')} (I)`}
       icon={<PiEyedropperBold />}
       colorScheme={isSelected ? 'invokeBlue' : 'base'}
-      variant="solid"
+      variant="outline"
       onClick={selectColorPicker}
-      isDisabled={isSelected}
+      isDisabled={isDisabled}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolEraserButton.tsx

@@ -1,16 +1,28 @@
 import { IconButton } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useSelectTool, useToolIsSelected } from 'features/controlLayers/components/Tool/hooks';
-import { memo } from 'react';
+import { useIsFiltering } from 'features/controlLayers/hooks/useIsFiltering';
+import { useIsTransforming } from 'features/controlLayers/hooks/useIsTransforming';
+import { selectIsStaging } from 'features/controlLayers/store/canvasSessionSlice';
+import { selectIsSelectedEntityDrawable } from 'features/controlLayers/store/selectors';
+import { memo, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiEraserBold } from 'react-icons/pi';
 
 export const ToolEraserButton = memo(() => {
   const { t } = useTranslation();
-  const isSelected = useToolIsSelected('eraser');
+  const isFiltering = useIsFiltering();
+  const isTransforming = useIsTransforming();
+  const isStaging = useAppSelector(selectIsStaging);
   const selectEraser = useSelectTool('eraser');
+  const isSelected = useToolIsSelected('eraser');
+  const isDrawingToolAllowed = useAppSelector(selectIsSelectedEntityDrawable);
+  const isDisabled = useMemo(() => {
+    return isTransforming || isFiltering || isStaging || !isDrawingToolAllowed;
+  }, [isDrawingToolAllowed, isFiltering, isStaging, isTransforming]);
 
-  useHotkeys('e', selectEraser, { enabled: !isSelected }, [isSelected, selectEraser]);
+  useHotkeys('e', selectEraser, { enabled: !isDisabled || isSelected }, [isDisabled, isSelected, selectEraser]);
 
   return (
     <IconButton
@@ -18,9 +30,9 @@ export const ToolEraserButton = memo(() => {
       tooltip={`${t('controlLayers.tool.eraser')} (E)`}
       icon={<PiEraserBold />}
       colorScheme={isSelected ? 'invokeBlue' : 'base'}
-      variant="solid"
+      variant="outline"
       onClick={selectEraser}
-      isDisabled={isSelected}
+      isDisabled={isDisabled}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolEraserWidth.tsx

@@ -1,12 +1,9 @@
 import {
+  CompositeNumberInput,
   CompositeSlider,
   FormControl,
   FormLabel,
-  IconButton,
-  NumberInput,
-  NumberInputField,
   Popover,
-  PopoverAnchor,
   PopoverArrow,
   PopoverBody,
   PopoverContent,
@@ -14,175 +11,47 @@ import {
 } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { useToolIsSelected } from 'features/controlLayers/components/Tool/hooks';
-import {
-  selectCanvasSettingsSlice,
-  settingsEraserWidthChanged,
-} from 'features/controlLayers/store/canvasSettingsSlice';
-import { clamp } from 'lodash-es';
-import type { KeyboardEvent } from 'react';
-import { memo, useCallback, useEffect, useState } from 'react';
-import { useHotkeys } from 'react-hotkeys-hook';
+import { eraserWidthChanged, selectToolSlice } from 'features/controlLayers/store/toolSlice';
+import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiCaretDownBold } from 'react-icons/pi';
 
-const selectEraserWidth = createSelector(selectCanvasSettingsSlice, (settings) => settings.eraserWidth);
+const marks = [0, 100, 200, 300];
 const formatPx = (v: number | string) => `${v} px`;
-
-function mapSliderValueToRawValue(value: number) {
-  if (value <= 40) {
-    // 0 to 40 on the slider -> 1px to 50px
-    return 1 + (49 * value) / 40;
-  } else if (value <= 70) {
-    // 40 to 70 on the slider -> 50px to 200px
-    return 50 + (150 * (value - 40)) / 30;
-  } else {
-    // 70 to 100 on the slider -> 200px to 600px
-    return 200 + (400 * (value - 70)) / 30;
-  }
-}
-
-function mapRawValueToSliderValue(value: number) {
-  if (value <= 50) {
-    // 1px to 50px -> 0 to 40 on the slider
-    return ((value - 1) * 40) / 49;
-  } else if (value <= 200) {
-    // 50px to 200px -> 40 to 70 on the slider
-    return 40 + ((value - 50) * 30) / 150;
-  } else {
-    // 200px to 600px -> 70 to 100 on the slider
-    return 70 + ((value - 200) * 30) / 400;
-  }
-}
-
-function formatSliderValue(value: number) {
-  return `${String(mapSliderValueToRawValue(value))} px`;
-}
-
-const marks = [
-  mapRawValueToSliderValue(1),
-  mapRawValueToSliderValue(50),
-  mapRawValueToSliderValue(200),
-  mapRawValueToSliderValue(600),
-];
-
-const sliderDefaultValue = mapRawValueToSliderValue(50);
+const selectEraserWidth = createSelector(selectToolSlice, (tool) => tool.eraser.width);
 
 export const ToolEraserWidth = memo(() => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
-  const isSelected = useToolIsSelected('eraser');
   const width = useAppSelector(selectEraserWidth);
-  const [localValue, setLocalValue] = useState(width);
   const onChange = useCallback(
     (v: number) => {
-      dispatch(settingsEraserWidthChanged(clamp(Math.round(v), 1, 600)));
+      dispatch(eraserWidthChanged(Math.round(v)));
     },
     [dispatch]
   );
-
-  const increment = useCallback(() => {
-    let newWidth = Math.round(width * 1.15);
-    if (newWidth === width) {
-      newWidth += 1;
-    }
-    onChange(newWidth);
-  }, [onChange, width]);
-
-  const decrement = useCallback(() => {
-    let newWidth = Math.round(width * 0.85);
-    if (newWidth === width) {
-      newWidth -= 1;
-    }
-    onChange(newWidth);
-  }, [onChange, width]);
-
-  const onChangeSlider = useCallback(
-    (value: number) => {
-      onChange(mapSliderValueToRawValue(value));
-    },
-    [onChange]
-  );
-
-  const onBlur = useCallback(() => {
-    if (isNaN(Number(localValue))) {
-      onChange(50);
-      setLocalValue(50);
-    } else {
-      onChange(localValue);
-    }
-  }, [localValue, onChange]);
-
-  const onChangeNumberInput = useCallback((valueAsString: string, valueAsNumber: number) => {
-    setLocalValue(valueAsNumber);
-  }, []);
-
-  const onKeyDown = useCallback(
-    (e: KeyboardEvent<HTMLInputElement>) => {
-      if (e.key === 'Enter') {
-        onBlur();
-      }
-    },
-    [onBlur]
-  );
-
-  useEffect(() => {
-    setLocalValue(width);
-  }, [width]);
-
-  useHotkeys('[', decrement, { enabled: isSelected }, [decrement, isSelected]);
-  useHotkeys(']', increment, { enabled: isSelected }, [increment, isSelected]);
-
   return (
-    <Popover>
-      <FormControl w="min-content" gap={2}>
-        <FormLabel m={0}>{t('controlLayers.width')}</FormLabel>
-        <PopoverAnchor>
-          <NumberInput
-            display="flex"
-            alignItems="center"
+    <FormControl w="min-content" gap={2}>
+      <FormLabel m={0}>{t('controlLayers.width')}</FormLabel>
+      <Popover isLazy>
+        <PopoverTrigger>
+          <CompositeNumberInput
             min={1}
             max={600}
-            value={localValue}
-            onChange={onChangeNumberInput}
-            onBlur={onBlur}
-            w="76px"
-            format={formatPx}
             defaultValue={50}
-            onKeyDown={onKeyDown}
-            clampValueOnBlur={false}
-          >
-            <NumberInputField paddingInlineEnd={7} />
-            <PopoverTrigger>
-              <IconButton
-                aria-label="open-slider"
-                icon={<PiCaretDownBold />}
-                size="sm"
-                variant="link"
-                position="absolute"
-                insetInlineEnd={0}
-                h="full"
-              />
-            </PopoverTrigger>
-          </NumberInput>
-        </PopoverAnchor>
-      </FormControl>
-      <PopoverContent w={200} pt={0} pb={2} px={4}>
-        <PopoverArrow />
-        <PopoverBody>
-          <CompositeSlider
-            min={0}
-            max={100}
-            value={mapRawValueToSliderValue(localValue)}
-            onChange={onChangeSlider}
-            defaultValue={sliderDefaultValue}
-            marks={marks}
-            formatValue={formatSliderValue}
-            alwaysShowMarks
+            value={width}
+            onChange={onChange}
+            w={24}
+            format={formatPx}
           />
-        </PopoverBody>
-      </PopoverContent>
-    </Popover>
+        </PopoverTrigger>
+        <PopoverContent w={200} py={2} px={4}>
+          <PopoverArrow />
+          <PopoverBody>
+            <CompositeSlider min={1} max={300} defaultValue={50} value={width} onChange={onChange} marks={marks} />
+          </PopoverBody>
+        </PopoverContent>
+      </Popover>
+    </FormControl>
   );
 });
 

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolFillColorPicker.tsx

@@ -3,20 +3,20 @@ import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIColorPicker from 'common/components/IAIColorPicker';
 import { rgbaColorToString } from 'common/util/colorCodeTransformers';
-import { selectCanvasSettingsSlice, settingsColorChanged } from 'features/controlLayers/store/canvasSettingsSlice';
+import { fillChanged, selectToolSlice } from 'features/controlLayers/store/toolSlice';
 import type { RgbaColor } from 'features/controlLayers/store/types';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-const selectColor = createSelector(selectCanvasSettingsSlice, (settings) => settings.color);
+const selectFill = createSelector(selectToolSlice, (tool) => tool.fill);
 
-export const ToolColorPicker = memo(() => {
+export const ToolFillColorPicker = memo(() => {
   const { t } = useTranslation();
-  const fill = useAppSelector(selectColor);
+  const fill = useAppSelector(selectFill);
   const dispatch = useAppDispatch();
   const onChange = useCallback(
     (color: RgbaColor) => {
-      dispatch(settingsColorChanged(color));
+      dispatch(fillChanged(color));
     },
     [dispatch]
   );
@@ -40,4 +40,4 @@ export const ToolColorPicker = memo(() => {
   );
 });
 
-ToolColorPicker.displayName = 'ToolFillColorPicker';
+ToolFillColorPicker.displayName = 'ToolFillColorPicker';

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolMoveButton.tsx

@@ -1,16 +1,28 @@
 import { IconButton } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useSelectTool, useToolIsSelected } from 'features/controlLayers/components/Tool/hooks';
-import { memo } from 'react';
+import { useIsFiltering } from 'features/controlLayers/hooks/useIsFiltering';
+import { useIsTransforming } from 'features/controlLayers/hooks/useIsTransforming';
+import { selectIsStaging } from 'features/controlLayers/store/canvasSessionSlice';
+import { selectIsSelectedEntityDrawable } from 'features/controlLayers/store/selectors';
+import { memo, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiCursorBold } from 'react-icons/pi';
 
 export const ToolMoveButton = memo(() => {
   const { t } = useTranslation();
-  const isSelected = useToolIsSelected('move');
+  const isFiltering = useIsFiltering();
+  const isTransforming = useIsTransforming();
   const selectMove = useSelectTool('move');
+  const isSelected = useToolIsSelected('move');
+  const isStaging = useAppSelector(selectIsStaging);
+  const isDrawingToolAllowed = useAppSelector(selectIsSelectedEntityDrawable);
+  const isDisabled = useMemo(() => {
+    return isTransforming || isFiltering || isStaging || !isDrawingToolAllowed;
+  }, [isDrawingToolAllowed, isFiltering, isStaging, isTransforming]);
 
-  useHotkeys('v', selectMove, { enabled: !isSelected }, [isSelected, selectMove]);
+  useHotkeys('v', selectMove, { enabled: !isDisabled || isSelected }, [isDisabled, isSelected, selectMove]);
 
   return (
     <IconButton
@@ -18,9 +30,9 @@ export const ToolMoveButton = memo(() => {
       tooltip={`${t('controlLayers.tool.move')} (V)`}
       icon={<PiCursorBold />}
       colorScheme={isSelected ? 'invokeBlue' : 'base'}
-      variant="solid"
+      variant="outline"
       onClick={selectMove}
-      isDisabled={isSelected}
+      isDisabled={isDisabled}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolRectButton.tsx

@@ -1,16 +1,29 @@
 import { IconButton } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useSelectTool, useToolIsSelected } from 'features/controlLayers/components/Tool/hooks';
-import { memo } from 'react';
+import { useIsFiltering } from 'features/controlLayers/hooks/useIsFiltering';
+import { useIsTransforming } from 'features/controlLayers/hooks/useIsTransforming';
+import { selectIsStaging } from 'features/controlLayers/store/canvasSessionSlice';
+import { selectIsSelectedEntityDrawable } from 'features/controlLayers/store/selectors';
+import { memo, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiRectangleBold } from 'react-icons/pi';
 
 export const ToolRectButton = memo(() => {
   const { t } = useTranslation();
-  const isSelected = useToolIsSelected('rect');
   const selectRect = useSelectTool('rect');
+  const isSelected = useToolIsSelected('rect');
+  const isFiltering = useIsFiltering();
+  const isTransforming = useIsTransforming();
+  const isStaging = useAppSelector(selectIsStaging);
+  const isDrawingToolAllowed = useAppSelector(selectIsSelectedEntityDrawable);
 
-  useHotkeys('u', selectRect, { enabled: !isSelected }, [isSelected, selectRect]);
+  const isDisabled = useMemo(() => {
+    return isTransforming || isFiltering || isStaging || !isDrawingToolAllowed;
+  }, [isDrawingToolAllowed, isFiltering, isStaging, isTransforming]);
+
+  useHotkeys('u', selectRect, { enabled: !isDisabled || isSelected }, [isDisabled, isSelected, selectRect]);
 
   return (
     <IconButton
@@ -18,9 +31,9 @@ export const ToolRectButton = memo(() => {
       tooltip={`${t('controlLayers.tool.rectangle')} (U)`}
       icon={<PiRectangleBold />}
       colorScheme={isSelected ? 'invokeBlue' : 'base'}
-      variant="solid"
+      variant="outline"
       onClick={selectRect}
-      isDisabled={isSelected}
+      isDisabled={isDisabled}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/Tool/ToolSettings.tsx

@@ -6,7 +6,7 @@ import { memo } from 'react';
 
 export const ToolSettings = memo(() => {
   const canvasManager = useCanvasManager();
-  const tool = useStore(canvasManager.tool.$tool);
+  const tool = useStore(canvasManager.stateApi.$tool);
   if (tool === 'brush') {
     return <ToolBrushWidth />;
   }