tidy(ui): organise tool module

- Canvas generation mode is replace with a boolean `sendToCanvas` flag. When off, images generated on the canvas go to the gallery. When on, they get added to the staging area.
- When an image result is received, if its destination is the canvas, staging is automatically started.
- Updated queue list to show the destination column.
- Added `IconSwitch` component to represent binary choices, used for the new `sendToCanvas` flag and image viewer toggle.
- Remove the queue actions menu in `QueueControls`. Move the queue count badge to the cancel button.
- Redo layout of `QueueControls` to prevent duplicate queue count badges.
- Fix issue where gallery and options panels could show thru transparent regions of queue tab.
- Disable panel hotkeys when on mm/queue tabs.

.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/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

@@ -45,13 +45,11 @@ class UIType(str, Enum, metaclass=MetaEnum):
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
     VAEModel = "VAEModelField"
-    FluxVAEModel = "FluxVAEModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
     IPAdapterModel = "IPAdapterModelField"
     T2IAdapterModel = "T2IAdapterModelField"
     T5EncoderModel = "T5EncoderModelField"
-    CLIPEmbedModel = "CLIPEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -130,7 +128,6 @@ class FieldDescriptions:
     noise = "Noise tensor"
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
     t5_encoder = "T5 tokenizer and text encoder"
-    clip_embed_model = "CLIP Embed loader"
     unet = "UNet (scheduler, LoRAs)"
     transformer = "Transformer"
     vae = "VAE"
@@ -181,7 +178,7 @@ 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_encoder.py

@@ -40,10 +40,7 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> FluxConditioningOutput:
-        # Note: The T5 and CLIP encoding are done in separate functions to ensure that all model references are locally
-        # scoped. This ensures that the T5 model can be freed and gc'd before loading the CLIP model (if necessary).
-        t5_embeddings = self._t5_encode(context)
-        clip_embeddings = self._clip_encode(context)
+        t5_embeddings, clip_embeddings = self._encode_prompt(context)
         conditioning_data = ConditioningFieldData(
             conditionings=[FLUXConditioningInfo(clip_embeds=clip_embeddings, t5_embeds=t5_embeddings)]
         )
@@ -51,7 +48,12 @@ class FluxTextEncoderInvocation(BaseInvocation):
         conditioning_name = context.conditioning.save(conditioning_data)
         return FluxConditioningOutput.build(conditioning_name)
 
-    def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
+    def _encode_prompt(self, context: InvocationContext) -> tuple[torch.Tensor, torch.Tensor]:
+        # Load CLIP.
+        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
+        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
+
+        # Load T5.
         t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
         t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
 
@@ -68,15 +70,6 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             prompt_embeds = t5_encoder(prompt)
 
-        assert isinstance(prompt_embeds, torch.Tensor)
-        return prompt_embeds
-
-    def _clip_encode(self, context: InvocationContext) -> torch.Tensor:
-        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
-        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
-
-        prompt = [self.prompt]
-
         with (
             clip_text_encoder_info as clip_text_encoder,
             clip_tokenizer_info as clip_tokenizer,
@@ -88,5 +81,6 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             pooled_prompt_embeds = clip_encoder(prompt)
 
+        assert isinstance(prompt_embeds, torch.Tensor)
         assert isinstance(pooled_prompt_embeds, torch.Tensor)
-        return pooled_prompt_embeds
+        return prompt_embeds, pooled_prompt_embeds

invokeai/app/invocations/flux_text_to_image.py

@@ -0,0 +1,172 @@
+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:
+        # 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)
+
+        latents = self._run_diffusion(context, flux_conditioning.clip_embeds, flux_conditioning.t5_embeds)
+        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,
+        clip_embeddings: torch.Tensor,
+        t5_embeddings: torch.Tensor,
+    ):
+        transformer_info = context.models.load(self.transformer.transformer)
+        inference_dtype = torch.bfloat16
+
+        # 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,
+        )
+
+        img, 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=img.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())
+
+        # HACK(ryand): Manually empty the cache. Currently we don't check the size of the model before loading it from
+        # disk. Since the transformer model is large (24GB), there's a good chance that it will OOM on 32GB RAM systems
+        # if the cache is not empty.
+        context.models._services.model_manager.load.ram_cache.make_room(24 * 2**30)
+
+        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=img,
+                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/invocations/model.py

@@ -157,7 +157,7 @@ class FluxModelLoaderOutput(BaseInvocationOutput):
     title="Flux Main Model",
     tags=["model", "flux"],
     category="model",
-    version="1.0.4",
+    version="1.0.3",
     classification=Classification.Prototype,
 )
 class FluxModelLoaderInvocation(BaseInvocation):
@@ -169,35 +169,23 @@ class FluxModelLoaderInvocation(BaseInvocation):
         input=Input.Direct,
     )
 
-    t5_encoder_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
-    )
-
-    clip_embed_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPEmbedModel,
+    t5_encoder: ModelIdentifierField = InputField(
+        description=FieldDescriptions.t5_encoder,
+        ui_type=UIType.T5EncoderModel,
         input=Input.Direct,
-        title="CLIP Embed",
-    )
-
-    vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
     )
 
     def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
-        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
-            if not context.models.exists(key):
-                raise ValueError(f"Unknown model: {key}")
-
-        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
-
-        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-
-        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        model_key = self.model.key
 
+        if not context.models.exists(model_key):
+            raise ValueError(f"Unknown model: {model_key}")
+        transformer = self._get_model(context, SubModelType.Transformer)
+        tokenizer = self._get_model(context, SubModelType.Tokenizer)
+        tokenizer2 = self._get_model(context, SubModelType.Tokenizer2)
+        clip_encoder = self._get_model(context, SubModelType.TextEncoder)
+        t5_encoder = self._get_model(context, SubModelType.TextEncoder2)
+        vae = self._get_model(context, SubModelType.VAE)
         transformer_config = context.models.get_config(transformer)
         assert isinstance(transformer_config, CheckpointConfigBase)
 
@@ -209,6 +197,52 @@ class FluxModelLoaderInvocation(BaseInvocation):
             max_seq_len=max_seq_lengths[transformer_config.config_path],
         )
 
+    def _get_model(self, context: InvocationContext, submodel: SubModelType) -> ModelIdentifierField:
+        match submodel:
+            case SubModelType.Transformer:
+                return self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+            case SubModelType.VAE:
+                return self._pull_model_from_mm(
+                    context,
+                    SubModelType.VAE,
+                    "FLUX.1-schnell_ae",
+                    ModelType.VAE,
+                    BaseModelType.Flux,
+                )
+            case submodel if submodel in [SubModelType.Tokenizer, SubModelType.TextEncoder]:
+                return self._pull_model_from_mm(
+                    context,
+                    submodel,
+                    "clip-vit-large-patch14",
+                    ModelType.CLIPEmbed,
+                    BaseModelType.Any,
+                )
+            case submodel if submodel in [SubModelType.Tokenizer2, SubModelType.TextEncoder2]:
+                return self._pull_model_from_mm(
+                    context,
+                    submodel,
+                    self.t5_encoder.name,
+                    ModelType.T5Encoder,
+                    BaseModelType.Any,
+                )
+            case _:
+                raise Exception(f"{submodel.value} is not a supported submodule for a flux model")
+
+    def _pull_model_from_mm(
+        self,
+        context: InvocationContext,
+        submodel: SubModelType,
+        name: str,
+        type: ModelType,
+        base: BaseModelType,
+    ):
+        if models := context.models.search_by_attrs(name=name, base=base, type=type):
+            if len(models) != 1:
+                raise Exception(f"Multiple models detected for selected model with name {name}")
+            return ModelIdentifierField.from_config(models[0]).model_copy(update={"submodel_type": submodel})
+        else:
+            raise ValueError(f"Please install the {base}:{type} model named {name} via starter models")
+
 
 @invocation(
     "main_model_loader",

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

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 {
   "name": "FLUX Text to Image",
   "author": "InvokeAI",
-  "description": "A simple text-to-image workflow using FLUX dev or schnell models.",
-  "version": "1.0.4",
+  "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": "1.0.0",
   "contact": "",
   "tags": "text2image, flux",
   "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 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
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@@ -38,127 +30,12 @@
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@@ -167,25 +44,31 @@
         "inputs": {
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+        "type": "flux_text_to_image",
+        "version": "1.0.0",
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+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": false,
+        "useCache": true,
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-      "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",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
       "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",
+      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33avae-159bdf1b-79e7-4174-b86e-d40e646964c8vae",
       "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    },
+    {
+      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33atransformer-159bdf1b-79e7-4174-b86e-d40e646964c8transformer",
+      "type": "default",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
+      "sourceHandle": "transformer",
+      "targetHandle": "transformer"
+    },
+    {
+      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33at5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
+      "type": "default",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
       "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",
+      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33aclip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
       "type": "default",
-      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "clip",
       "targetHandle": "clip"
+    },
+    {
+      "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 +1,176 @@
+# 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,
+):
+    dtype = model.txt_in.bias.dtype
+
+    # TODO(ryand): This shouldn't be necessary if we manage the dtypes properly in the caller.
+    img = img.to(dtype=dtype)
+    img_ids = img_ids.to(dtype=dtype)
+    txt = txt.to(dtype=dtype)
+    txt_ids = txt_ids.to(dtype=dtype)
+    vec = vec.to(dtype=dtype)
+
+    # this 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)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device)[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

@@ -66,14 +66,12 @@ 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
 
         config.path = str(self._get_model_path(config))
-        self._ram_cache.make_room(self.get_size_fs(config, Path(config.path), submodel_type))
         loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(
@@ -85,7 +83,7 @@ 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
@@ -210,6 +193,15 @@ class ModelCacheBase(ABC, Generic[T]):
         """
         pass
 
+    @abstractmethod
+    def exists(
+        self,
+        key: str,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> bool:
+        """Return true if the model identified by key and submodel_type is in the cache."""
+        pass
+
     @abstractmethod
     def cache_size(self) -> int:
         """Get the total size of the models currently cached."""

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

@@ -1,6 +1,22 @@
 # Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
 # TODO: Add Stalker's proper name to copyright
-""" """
+"""
+Manage a RAM cache of diffusion/transformer models for fast switching.
+They are moved between GPU VRAM and CPU RAM as necessary. If the cache
+grows larger than a preset maximum, then the least recently used
+model will be cleared and (re)loaded from disk when next needed.
+
+The cache returns context manager generators designed to load the
+model into the GPU within the context, and unload outside the
+context. Use like this:
+
+   cache = ModelCache(max_cache_size=7.5)
+   with cache.get_model('runwayml/stable-diffusion-1-5') as SD1,
+          cache.get_model('stabilityai/stable-diffusion-2') as SD2:
+       do_something_in_GPU(SD1,SD2)
+
+
+"""
 
 import gc
 import math
@@ -24,74 +40,53 @@ from invokeai.backend.model_manager.load.model_util import calc_model_size_by_da
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
-# Size of a GB in bytes.
-GB = 2**30
+# Maximum size of the cache, in gigs
+# Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
+DEFAULT_MAX_CACHE_SIZE = 6.0
+
+# amount of GPU memory to hold in reserve for use by generations (GB)
+DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
+
+# actual size of a gig
+GIG = 1073741824
 
 # Size of a MB in bytes.
 MB = 2**20
 
 
 class ModelCache(ModelCacheBase[AnyModel]):
-    """A cache for managing models in memory.
-
-    The cache is based on two levels of model storage:
-    - execution_device: The device where most models are executed (typically "cuda", "mps", or "cpu").
-    - storage_device: The device where models are offloaded when not in active use (typically "cpu").
-
-    The model cache is based on the following assumptions:
-    - storage_device_mem_size > execution_device_mem_size
-    - disk_to_storage_device_transfer_time >> storage_device_to_execution_device_transfer_time
-
-    A copy of all models in the cache is always kept on the storage_device. A subset of the models also have a copy on
-    the execution_device.
-
-    Models are moved between the storage_device and the execution_device as necessary. Cache size limits are enforced
-    on both the storage_device and the execution_device. The execution_device cache uses a smallest-first offload
-    policy. The storage_device cache uses a least-recently-used (LRU) offload policy.
-
-    Note: Neither of these offload policies has really been compared against alternatives. It's likely that different
-    policies would be better, although the optimal policies are likely heavily dependent on usage patterns and HW
-    configuration.
-
-    The cache returns context manager generators designed to load the model into the execution device (often GPU) within
-    the context, and unload outside the context.
-
-    Example usage:
-    ```
-    cache = ModelCache(max_cache_size=7.5, max_vram_cache_size=6.0)
-    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1:
-        do_something_on_gpu(SD1)
-    ```
-    """
+    """Implementation of ModelCacheBase."""
 
     def __init__(
         self,
-        max_cache_size: float,
-        max_vram_cache_size: float,
+        max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
+        max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
         execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
         precision: torch.dtype = torch.float16,
+        sequential_offload: bool = False,
         lazy_offloading: bool = True,
+        sha_chunksize: int = 16777216,
         log_memory_usage: bool = False,
         logger: Optional[Logger] = None,
     ):
         """
         Initialize the model RAM cache.
 
-        :param max_cache_size: Maximum size of the storage_device cache in GBs.
-        :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
+        :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
         :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 sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
         :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
+        self._precision: torch.dtype = precision
         self._max_cache_size: float = max_cache_size
         self._max_vram_cache_size: float = max_vram_cache_size
         self._execution_device: torch.device = execution_device
@@ -133,16 +128,6 @@ 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."""
@@ -160,6 +145,15 @@ class ModelCache(ModelCacheBase[AnyModel]):
             total += cache_record.size
         return total
 
+    def exists(
+        self,
+        key: str,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> bool:
+        """Return true if the model identified by key and submodel_type is in the cache."""
+        key = self._make_cache_key(key, submodel_type)
+        return key in self._cached_models
+
     def put(
         self,
         key: str,
@@ -209,7 +203,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         # more stats
         if self.stats:
             stats_name = stats_name or key
-            self.stats.cache_size = int(self._max_cache_size * GB)
+            self.stats.cache_size = int(self._max_cache_size * GIG)
             self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
             self.stats.in_cache = len(self._cached_models)
             self.stats.loaded_model_sizes[stats_name] = max(
@@ -237,13 +231,10 @@ class ModelCache(ModelCacheBase[AnyModel]):
             return model_key
 
     def offload_unlocked_models(self, size_required: int) -> None:
-        """Offload models from the execution_device to make room for size_required.
-
-        :param size_required: The amount of space to clear in the execution_device cache, in bytes.
-        """
-        reserved = self._max_vram_cache_size * GB
+        """Move any unused models from VRAM."""
+        reserved = self._max_vram_cache_size * GIG
         vram_in_use = torch.cuda.memory_allocated() + size_required
-        self.logger.debug(f"{(vram_in_use/GB):.2f}GB VRAM needed for models; max allowed={(reserved/GB):.2f}GB")
+        self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM needed for models; max allowed={(reserved/GIG):.2f}GB")
         for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
             if vram_in_use <= reserved:
                 break
@@ -254,7 +245,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 cache_entry.loaded = False
                 vram_in_use = torch.cuda.memory_allocated() + size_required
                 self.logger.debug(
-                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GB):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GB):.2f}GB"
+                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
                 )
 
         TorchDevice.empty_cache()
@@ -312,7 +303,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self.logger.debug(
             f"Moved model '{cache_entry.key}' from {source_device} to"
             f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
-            f"Estimated model size: {(cache_entry.size/GB):.3f} GB."
+            f"Estimated model size: {(cache_entry.size/GIG):.3f} GB."
             f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
         )
 
@@ -335,14 +326,14 @@ class ModelCache(ModelCacheBase[AnyModel]):
                     f"Moving model '{cache_entry.key}' from {source_device} to"
                     f" {target_device} caused an unexpected change in VRAM usage. The model's"
                     " estimated size may be incorrect. Estimated model size:"
-                    f" {(cache_entry.size/GB):.3f} GB.\n"
+                    f" {(cache_entry.size/GIG):.3f} GB.\n"
                     f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
                 )
 
     def print_cuda_stats(self) -> None:
         """Log CUDA diagnostics."""
-        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GB)
-        ram = "%4.2fG" % (self.cache_size() / GB)
+        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
+        ram = "%4.2fG" % (self.cache_size() / GIG)
 
         in_ram_models = 0
         in_vram_models = 0
@@ -362,20 +353,17 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 )
 
     def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size.
-
-        Note: This function deletes all of the cache's internal references to a model in order to free it. If there are
-        external references to the model, there's nothing that the cache can do about it, and those models will not be
-        garbage-collected.
-        """
+        """Make enough room in the cache to accommodate a new model of indicated size."""
+        # calculate how much memory this model will require
+        # multiplier = 2 if self.precision==torch.float32 else 1
         bytes_needed = size
-        maximum_size = self.max_cache_size * GB  # stored in GB, convert to bytes
+        maximum_size = self.max_cache_size * GIG  # stored in GB, convert to bytes
         current_size = self.cache_size()
 
         if current_size + bytes_needed > maximum_size:
             self.logger.debug(
-                f"Max cache size exceeded: {(current_size/GB):.2f}/{self.max_cache_size:.2f} GB, need an additional"
-                f" {(bytes_needed/GB):.2f} GB"
+                f"Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional"
+                f" {(bytes_needed/GIG):.2f} GB"
             )
 
         self.logger.debug(f"Before making_room: cached_models={len(self._cached_models)}")
@@ -392,7 +380,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
 
             if not cache_entry.locked:
                 self.logger.debug(
-                    f"Removing {model_key} from RAM cache to free at least {(size/GB):.2f} GB (-{(cache_entry.size/GB):.2f} GB)"
+                    f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
                 )
                 current_size -= cache_entry.size
                 models_cleared += 1

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

@@ -32,7 +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:
@@ -191,8 +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)
             model.load_state_dict(sd, assign=True)
         return model
 
@@ -233,7 +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/backend/quantization/bnb_llm_int8.py

@@ -54,10 +54,8 @@ class InvokeLinear8bitLt(bnb.nn.Linear8bitLt):
 
         # See `bnb.nn.Linear8bitLt._save_to_state_dict()` for the serialization logic of SCB and weight_format.
         scb = state_dict.pop(prefix + "SCB", None)
-
-        # Currently, we only support weight_format=0.
-        weight_format = state_dict.pop(prefix + "weight_format", None)
-        assert weight_format == 0
+        # weight_format is unused, but we pop it so we can validate that there are no unexpected keys.
+        _weight_format = state_dict.pop(prefix + "weight_format", None)
 
         # TODO(ryand): Technically, we should be using `strict`, `missing_keys`, `unexpected_keys`, and `error_msgs`
         # rather than raising an exception to correctly implement this API.
@@ -91,14 +89,6 @@ class InvokeLinear8bitLt(bnb.nn.Linear8bitLt):
             )
             self.bias = bias if bias is None else torch.nn.Parameter(bias)
 
-        # Reset the state. The persisted fields are based on the initialization behaviour in
-        # `bnb.nn.Linear8bitLt.__init__()`.
-        new_state = bnb.MatmulLtState()
-        new_state.threshold = self.state.threshold
-        new_state.has_fp16_weights = False
-        new_state.use_pool = self.state.use_pool
-        self.state = new_state
-
 
 def _convert_linear_layers_to_llm_8bit(
     module: torch.nn.Module, ignore_modules: set[str], outlier_threshold: float, prefix: str = ""

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -43,11 +43,6 @@ class FLUXConditioningInfo:
     clip_embeds: torch.Tensor
     t5_embeds: torch.Tensor
 
-    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
-        self.clip_embeds = self.clip_embeds.to(device=device, dtype=dtype)
-        self.t5_embeds = self.t5_embeds.to(device=device, dtype=dtype)
-        return self
-
 
 @dataclass
 class ConditioningFieldData:

invokeai/backend/util/__init__.py

@@ -3,9 +3,10 @@ Initialization file for invokeai.backend.util
 """
 
 from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.backend.util.util import Chdir, directory_size
+from invokeai.backend.util.util import GIG, Chdir, directory_size
 
 __all__ = [
+    "GIG",
     "directory_size",
     "Chdir",
     "InvokeAILogger",

invokeai/backend/util/util.py

@@ -7,6 +7,9 @@ from pathlib import Path
 
 from PIL import Image
 
+# actual size of a gig
+GIG = 1073741824
+
 
 def slugify(value: str, allow_unicode: bool = False) -> str:
     """

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/en.json

@@ -706,8 +706,6 @@
         "availableModels": "Available Models",
         "baseModel": "Base Model",
         "cancel": "Cancel",
-        "clipEmbed": "CLIP Embed",
-        "clipVision": "CLIP Vision",
         "config": "Config",
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
@@ -795,7 +793,6 @@
         "settings": "Settings",
         "simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
         "source": "Source",
-        "spandrelImageToImage": "Image to Image (Spandrel)",
         "starterModels": "Starter Models",
         "starterModelsInModelManager": "Starter Models can be found in Model Manager",
         "syncModels": "Sync Models",
@@ -804,7 +801,6 @@
         "loraTriggerPhrases": "LoRA Trigger Phrases",
         "mainModelTriggerPhrases": "Main Model Trigger Phrases",
         "typePhraseHere": "Type phrase here",
-        "t5Encoder": "T5 Encoder",
         "upcastAttention": "Upcast Attention",
         "uploadImage": "Upload Image",
         "urlOrLocalPath": "URL or Local Path",
@@ -1654,15 +1650,6 @@
         "storeNotInitialized": "Store is not initialized"
     },
     "controlLayers": {
-        "bookmark": "Bookmark for Quick Switch",
-        "removeBookmark": "Remove Bookmark",
-        "saveCanvasToGallery": "Save Canvas To Gallery",
-        "saveBboxToGallery": "Save Bbox To Gallery",
-        "savedToGalleryOk": "Saved to Gallery",
-        "savedToGalleryError": "Error saving to gallery",
-        "mergeVisible": "Merge Visible",
-        "mergeVisibleOk": "Merged visible layers",
-        "mergeVisibleError": "Error merging visible layers",
         "clearHistory": "Clear History",
         "generateMode": "Generate",
         "generateModeDesc": "Create individual images. Generated images are added directly to the gallery.",
@@ -1674,7 +1661,6 @@
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
-        "compositeMaskedRegions": "Composite Masked Regions",
         "addLayer": "Add Layer",
         "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
@@ -1728,12 +1714,12 @@
         "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)",
@@ -1746,8 +1732,8 @@
         "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",
@@ -1783,6 +1769,7 @@
             "bbox": "Bbox",
             "move": "Move",
             "view": "View",
+            "transform": "Transform",
             "colorPicker": "Color Picker"
         },
         "filter": {
@@ -1792,13 +1779,6 @@
             "preview": "Preview",
             "apply": "Apply",
             "cancel": "Cancel"
-        },
-        "transform": {
-            "transform": "Transform",
-            "fitToBbox": "Fit to Bbox",
-            "reset": "Reset",
-            "apply": "Apply",
-            "cancel": "Cancel"
         }
     },
     "upscaling": {

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

@@ -16,7 +16,6 @@ import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicP
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
 import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
 import { StylePresetModal } from 'features/stylePresets/components/StylePresetForm/StylePresetModal';
-import { activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import RefreshAfterResetModal from 'features/system/components/SettingsModal/RefreshAfterResetModal';
 import SettingsModal from 'features/system/components/SettingsModal/SettingsModal';
 import { configChanged } from 'features/system/store/configSlice';
@@ -44,17 +43,10 @@ interface Props {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  selectedStylePresetId?: string;
-  destination?: TabName;
+  destination?: TabName | undefined;
 }
 
-const App = ({
-  config = DEFAULT_CONFIG,
-  selectedImage,
-  selectedWorkflowId,
-  selectedStylePresetId,
-  destination,
-}: Props) => {
+const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, destination }: Props) => {
   const language = useAppSelector(selectLanguage);
   const logger = useLogger('system');
   const dispatch = useAppDispatch();
@@ -93,12 +85,6 @@ const App = ({
     }
   }, [selectedWorkflowId, getAndLoadWorkflow]);
 
-  useEffect(() => {
-    if (selectedStylePresetId) {
-      dispatch(activeStylePresetIdChanged(selectedStylePresetId));
-    }
-  }, [dispatch, selectedStylePresetId]);
-
   useEffect(() => {
     if (destination) {
       dispatch(setActiveTab(destination));

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

@@ -45,7 +45,6 @@ interface Props extends PropsWithChildren {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  selectedStylePresetId?: string;
   destination?: TabName;
   customStarUi?: CustomStarUi;
   socketOptions?: Partial<ManagerOptions & SocketOptions>;
@@ -67,7 +66,6 @@ const InvokeAIUI = ({
   queueId,
   selectedImage,
   selectedWorkflowId,
-  selectedStylePresetId,
   destination,
   customStarUi,
   socketOptions,
@@ -229,7 +227,6 @@ const InvokeAIUI = ({
                 config={config}
                 selectedImage={selectedImage}
                 selectedWorkflowId={selectedWorkflowId}
-                selectedStylePresetId={selectedStylePresetId}
                 destination={destination}
               />
             </AppDndContext>

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/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/useBoolean.ts

@@ -1,74 +1,52 @@
-import { useStore } from '@nanostores/react';
 import type { WritableAtom } from 'nanostores';
-import { atom } from 'nanostores';
-import { useCallback, useState } from 'react';
+import { useCallback, useMemo, useState } from 'react';
 
-type UseBoolean = {
-  isTrue: boolean;
-  setTrue: () => void;
-  setFalse: () => void;
-  set: (value: boolean) => void;
-  toggle: () => void;
-};
-
-/**
- * Creates a hook to manage a boolean state. The boolean is stored in a nanostores atom.
- * Returns a tuple containing the hook and the atom. Use this for global boolean state.
- * @param initialValue Initial value of the boolean
- */
-export const buildUseBoolean = (initialValue: boolean): [() => UseBoolean, WritableAtom<boolean>] => {
-  const $boolean = atom(initialValue);
-
-  const setTrue = () => {
-    $boolean.set(true);
-  };
-  const setFalse = () => {
-    $boolean.set(false);
-  };
-  const set = (value: boolean) => {
-    $boolean.set(value);
-  };
-  const toggle = () => {
-    $boolean.set(!$boolean.get());
-  };
-
-  const useBoolean = () => {
-    const isTrue = useStore($boolean);
-
-    return {
-      isTrue,
-      setTrue,
-      setFalse,
-      set,
-      toggle,
-    };
-  };
-
-  return [useBoolean, $boolean] as const;
-};
-
-/**
- * Hook to manage a boolean state. Use this for a local boolean state.
- * @param initialValue Initial value of the boolean
- */
 export const useBoolean = (initialValue: boolean) => {
   const [isTrue, set] = useState(initialValue);
+  const setTrue = useCallback(() => set(true), []);
+  const setFalse = useCallback(() => set(false), []);
+  const toggle = useCallback(() => set((v) => !v), []);
 
-  const setTrue = useCallback(() => {
-    set(true);
-  }, [set]);
-  const setFalse = useCallback(() => {
-    set(false);
-  }, [set]);
-  const toggle = useCallback(() => {
-    set((val) => !val);
-  }, [set]);
+  const api = useMemo(
+    () => ({
+      isTrue,
+      set,
+      setTrue,
+      setFalse,
+      toggle,
+    }),
+    [isTrue, set, setTrue, setFalse, toggle]
+  );
 
-  return {
-    isTrue,
-    setTrue,
-    setFalse,
-    set,
-    toggle,
+  return api;
+};
+
+export const buildUseBoolean = ($boolean: WritableAtom<boolean>) => {
+  return () => {
+    const setTrue = useCallback(() => {
+      $boolean.set(true);
+    }, []);
+    const setFalse = useCallback(() => {
+      $boolean.set(false);
+    }, []);
+    const set = useCallback((value: boolean) => {
+      $boolean.set(value);
+    }, []);
+    const toggle = useCallback(() => {
+      $boolean.set(!$boolean.get());
+    }, []);
+
+    const api = useMemo(
+      () => ({
+        setTrue,
+        setFalse,
+        set,
+        toggle,
+        $boolean,
+      }),
+      [set, setFalse, setTrue, toggle]
+    );
+
+    return api;
   };
 };

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

@@ -128,7 +128,7 @@ const createSelector = (templates: Templates, isConnected: boolean) =>
         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} (${layerType})`;
@@ -158,7 +158,7 @@ const createSelector = (templates: Templates, isConnected: boolean) =>
         canvas.ipAdapters.entities
           .filter((entity) => entity.isEnabled)
           .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;
@@ -186,7 +186,7 @@ const createSelector = (templates: Templates, isConnected: boolean) =>
         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} (${layerType})`;
@@ -223,7 +223,7 @@ const createSelector = (templates: Templates, isConnected: boolean) =>
         canvas.rasterLayers.entities
           .filter((entity) => entity.isEnabled)
           .forEach((entity, i) => {
-            const layerLiteral = i18n.t('controlLayers.layer_one');
+            const layerLiteral = i18n.t('controlLayers.layers_one');
             const layerNumber = i + 1;
             const layerType = i18n.t(LAYER_TYPE_TO_TKEY[entity.type]);
             const prefix = `${layerLiteral} #${layerNumber} (${layerType})`;

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/EntityListActionBarAddLayerMenuItems.tsx

@@ -1,6 +1,5 @@
 import { MenuItem } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
-import { useDefaultIPAdapter } from 'features/controlLayers/hooks/useLayerControlAdapter';
 import {
   controlLayerAdded,
   inpaintMaskAdded,
@@ -15,7 +14,6 @@ import { PiPlusBold } from 'react-icons/pi';
 export const CanvasEntityListMenuItems = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
-  const defaultIPAdapter = useDefaultIPAdapter();
   const addInpaintMask = useCallback(() => {
     dispatch(inpaintMaskAdded({ isSelected: true }));
   }, [dispatch]);
@@ -29,9 +27,8 @@ export const CanvasEntityListMenuItems = memo(() => {
     dispatch(controlLayerAdded({ isSelected: true }));
   }, [dispatch]);
   const addIPAdapter = useCallback(() => {
-    const overrides = { ipAdapter: defaultIPAdapter };
-    dispatch(ipaAdded({ isSelected: true, overrides }));
-  }, [defaultIPAdapter, dispatch]);
+    dispatch(ipaAdded({ isSelected: true }));
+  }, [dispatch]);
 
   return (
     <>
@@ -48,7 +45,7 @@ export const CanvasEntityListMenuItems = memo(() => {
         {t('controlLayers.controlLayer')}
       </MenuItem>
       <MenuItem icon={<PiPlusBold />} onClick={addIPAdapter}>
-        {t('controlLayers.globalIPAdapter')}
+        {t('controlLayers.ipAdapter')}
       </MenuItem>
     </>
   );

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);
 
@@ -70,7 +70,7 @@ 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
@@ -95,7 +95,7 @@ export const SelectedEntityOpacity = memo(() => {
       if (!$shift.get()) {
         snappedOpacity = snapToNearest(opacity, snapCandidates, 2);
       }
-      const mappedOpacity = mapSliderValueToRawValue(snappedOpacity);
+      const mappedOpacity = mapSliderValueToOpacity(snappedOpacity);
 
       dispatch(entityOpacityChanged({ entityIdentifier: selectedEntityIdentifier, opacity: mappedOpacity }));
     },
@@ -157,7 +157,7 @@ export const SelectedEntityOpacity = memo(() => {
             clampValueOnBlur={false}
             variant="outline"
           >
-            <NumberInputField paddingInlineEnd={7} _focusVisible={{ zIndex: 0 }} />
+            <NumberInputField paddingInlineEnd={7} />
             <PopoverTrigger>
               <IconButton
                 aria-label="open-slider"

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

@@ -1,22 +1,37 @@
-import { Divider, Flex } from '@invoke-ai/ui-library';
+import { Box, ContextMenu, Divider, Flex, MenuList } 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 { EntityListActionBar } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBar';
+import { CanvasEntityListMenuItems } from 'features/controlLayers/components/CanvasEntityList/EntityListActionBarAddLayerMenuItems';
 import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { selectHasEntities } from 'features/controlLayers/store/selectors';
-import { memo } from 'react';
+import { memo, useCallback } from 'react';
 
 export const CanvasPanelContent = memo(() => {
   const hasEntities = useAppSelector(selectHasEntities);
+  const renderMenu = useCallback(
+    () => (
+      <MenuList>
+        <CanvasEntityListMenuItems />
+      </MenuList>
+    ),
+    []
+  );
 
   return (
     <CanvasManagerProviderGate>
       <Flex flexDir="column" gap={2} w="full" h="full">
         <EntityListActionBar />
         <Divider py={0} />
-        {!hasEntities && <CanvasAddEntityButtons />}
-        {hasEntities && <CanvasEntityList />}
+        <ContextMenu<HTMLDivElement> renderMenu={renderMenu} stopImmediatePropagation stopPropagation>
+          {(ref) => (
+            <Box ref={ref} w="full" h="full">
+              {!hasEntities && <CanvasAddEntityButtons />}
+              {hasEntities && <CanvasEntityList />}
+            </Box>
+          )}
+        </ContextMenu>
       </Flex>
     </CanvasManagerProviderGate>
   );

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/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

@@ -47,7 +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 +1,37 @@
+/* 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 { 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 />
+        <CanvasSettingsPopover />
+        <ViewerToggle />
+      </Flex>
+    </CanvasManagerProviderGate>
+  );
+});
+
+ControlLayersToolbar.displayName = 'ControlLayersToolbar';

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/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/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 />;
   }

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

@@ -1,16 +1,26 @@
 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 { PiHandBold } from 'react-icons/pi';
 
 export const ToolViewButton = memo(() => {
   const { t } = useTranslation();
-  const isSelected = useToolIsSelected('view');
+  const isTransforming = useIsTransforming();
+  const isFiltering = useIsFiltering();
+  const isStaging = useAppSelector(selectIsStaging);
   const selectView = useSelectTool('view');
+  const isSelected = useToolIsSelected('view');
+  const isDisabled = useMemo(() => {
+    return isTransforming || isFiltering || isStaging;
+  }, [isFiltering, isStaging, isTransforming]);
 
-  useHotkeys('h', selectView, { enabled: !isSelected }, [selectView, isSelected]);
+  useHotkeys('h', selectView, { enabled: !isDisabled || isSelected }, [selectView, isSelected, isDisabled]);
 
   return (
     <IconButton
@@ -18,9 +28,9 @@ export const ToolViewButton = memo(() => {
       tooltip={`${t('controlLayers.tool.view')} (H)`}
       icon={<PiHandBold />}
       colorScheme={isSelected ? 'invokeBlue' : 'base'}
-      variant="solid"
+      variant="outline"
       onClick={selectView}
-      isDisabled={isSelected}
+      isDisabled={isDisabled}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/Tool/hooks.ts

@@ -6,14 +6,14 @@ import { useCallback } from 'react';
 
 export const useToolIsSelected = (tool: Tool) => {
   const canvasManager = useCanvasManager();
-  const isSelected = useStore(computed(canvasManager.tool.$tool, (t) => t === tool));
+  const isSelected = useStore(computed(canvasManager.stateApi.$tool, (t) => t === tool));
   return isSelected;
 };
 
 export const useSelectTool = (tool: Tool) => {
   const canvasManager = useCanvasManager();
   const setTool = useCallback(() => {
-    canvasManager.tool.$tool.set(tool);
-  }, [canvasManager.tool.$tool, tool]);
+    canvasManager.stateApi.$tool.set(tool);
+  }, [canvasManager.stateApi.$tool, tool]);
   return setTool;
 };

invokeai/frontend/web/src/features/controlLayers/components/Toolbar/CanvasToolbar.tsx

@@ -1,47 +0,0 @@
-/* eslint-disable i18next/no-literal-string */
-import { Flex, Spacer } from '@invoke-ai/ui-library';
-import { CanvasSettingsPopover } from 'features/controlLayers/components/Settings/CanvasSettingsPopover';
-import { ToolChooser } from 'features/controlLayers/components/Tool/ToolChooser';
-import { ToolColorPicker } from 'features/controlLayers/components/Tool/ToolFillColorPicker';
-import { ToolSettings } from 'features/controlLayers/components/Tool/ToolSettings';
-import { CanvasToolbarResetViewButton } from 'features/controlLayers/components/Toolbar/CanvasToolbarResetViewButton';
-import { CanvasToolbarSaveToGalleryButton } from 'features/controlLayers/components/Toolbar/CanvasToolbarSaveToGalleryButton';
-import { CanvasToolbarScale } from 'features/controlLayers/components/Toolbar/CanvasToolbarScale';
-import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { useCanvasDeleteLayerHotkey } from 'features/controlLayers/hooks/useCanvasDeleteLayerHotkey';
-import { useCanvasEntityQuickSwitchHotkey } from 'features/controlLayers/hooks/useCanvasEntityQuickSwitchHotkey';
-import { useCanvasResetLayerHotkey } from 'features/controlLayers/hooks/useCanvasResetLayerHotkey';
-import { useCanvasUndoRedoHotkeys } from 'features/controlLayers/hooks/useCanvasUndoRedoHotkeys';
-import { useNextPrevEntityHotkeys } from 'features/controlLayers/hooks/useNextPrevEntity';
-import { ToggleProgressButton } from 'features/gallery/components/ImageViewer/ToggleProgressButton';
-import { ViewerToggle } from 'features/gallery/components/ImageViewer/ViewerToggleMenu';
-import { memo } from 'react';
-
-export const CanvasToolbar = memo(() => {
-  useCanvasResetLayerHotkey();
-  useCanvasDeleteLayerHotkey();
-  useCanvasUndoRedoHotkeys();
-  useCanvasEntityQuickSwitchHotkey();
-  useNextPrevEntityHotkeys();
-
-  return (
-    <CanvasManagerProviderGate>
-      <Flex w="full" gap={2} alignItems="center">
-        <ToggleProgressButton />
-        <ToolChooser />
-        <Spacer />
-        <ToolSettings />
-        <Spacer />
-        <CanvasToolbarScale />
-        <CanvasToolbarResetViewButton />
-        <Spacer />
-        <ToolColorPicker />
-        <CanvasToolbarSaveToGalleryButton />
-        <CanvasSettingsPopover />
-        <ViewerToggle />
-      </Flex>
-    </CanvasManagerProviderGate>
-  );
-});
-
-CanvasToolbar.displayName = 'CanvasToolbar';

invokeai/frontend/web/src/features/controlLayers/components/Toolbar/CanvasToolbarSaveToGalleryButton.tsx

@@ -1,53 +0,0 @@
-import { IconButton, useShiftModifier } from '@invoke-ai/ui-library';
-import { logger } from 'app/logging/logger';
-import { buildUseBoolean } from 'common/hooks/useBoolean';
-import { isOk, withResultAsync } from 'common/util/result';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { toast } from 'features/toast/toast';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiFloppyDiskBold } from 'react-icons/pi';
-import { serializeError } from 'serialize-error';
-
-const log = logger('canvas');
-
-const [useIsSaving] = buildUseBoolean(false);
-
-export const CanvasToolbarSaveToGalleryButton = memo(() => {
-  const { t } = useTranslation();
-  const shift = useShiftModifier();
-  const canvasManager = useCanvasManager();
-  const isSaving = useIsSaving();
-
-  const onClick = useCallback(async () => {
-    isSaving.setTrue();
-
-    const rect = shift ? canvasManager.stateApi.getBbox().rect : canvasManager.stage.getVisibleRect('raster_layer');
-
-    const result = await withResultAsync(() =>
-      canvasManager.compositor.rasterizeAndUploadCompositeRasterLayer(rect, true)
-    );
-
-    if (isOk(result)) {
-      toast({ title: t('controlLayers.savedToGalleryOk') });
-    } else {
-      log.error({ error: serializeError(result.error) }, 'Failed to save canvas to gallery');
-      toast({ title: t('controlLayers.savedToGalleryError'), status: 'error' });
-    }
-
-    isSaving.setFalse();
-  }, [canvasManager.compositor, canvasManager.stage, canvasManager.stateApi, isSaving, shift, t]);
-
-  return (
-    <IconButton
-      variant="ghost"
-      onClick={onClick}
-      icon={<PiFloppyDiskBold />}
-      isLoading={isSaving.isTrue}
-      aria-label={shift ? t('controlLayers.saveBboxToGallery') : t('controlLayers.saveCanvasToGallery')}
-      tooltip={shift ? t('controlLayers.saveBboxToGallery') : t('controlLayers.saveCanvasToGallery')}
-    />
-  );
-});
-
-CanvasToolbarSaveToGalleryButton.displayName = 'CanvasToolbarSaveToGalleryButton';

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

@@ -1,13 +1,19 @@
 import { Button, ButtonGroup, Flex, Heading, Spacer } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import type { CanvasEntityAdapter } from 'features/controlLayers/konva/CanvasEntityAdapter/types';
+import {
+  EntityIdentifierContext,
+  useEntityIdentifierContext,
+} from 'features/controlLayers/contexts/EntityIdentifierContext';
+import { useEntityAdapter } from 'features/controlLayers/hooks/useEntityAdapter';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiArrowsCounterClockwiseBold, PiArrowsOutBold, PiCheckBold, PiXBold } from 'react-icons/pi';
+import { PiArrowsCounterClockwiseBold, PiCheckBold, PiXBold } from 'react-icons/pi';
 
-const TransformBox = memo(({ adapter }: { adapter: CanvasEntityAdapter }) => {
+const TransformBox = memo(() => {
   const { t } = useTranslation();
+  const entityIdentifier = useEntityIdentifierContext();
+  const adapter = useEntityAdapter(entityIdentifier);
   const isProcessing = useStore(adapter.transformer.$isProcessing);
 
   return (
@@ -24,19 +30,9 @@ const TransformBox = memo(({ adapter }: { adapter: CanvasEntityAdapter }) => {
       transitionDuration="normal"
     >
       <Heading size="md" color="base.300" userSelect="none">
-        {t('controlLayers.transform.transform')}
+        {t('controlLayers.tool.transform')}
       </Heading>
       <ButtonGroup isAttached={false} size="sm" w="full">
-        <Button
-          leftIcon={<PiArrowsOutBold />}
-          onClick={adapter.transformer.fitProxyRectToBbox}
-          isLoading={isProcessing}
-          loadingText={t('controlLayers.transform.reset')}
-          variant="ghost"
-        >
-          {t('controlLayers.transform.fitToBbox')}
-        </Button>
-        <Spacer />
         <Button
           leftIcon={<PiArrowsCounterClockwiseBold />}
           onClick={adapter.transformer.resetTransform}
@@ -44,8 +40,9 @@ const TransformBox = memo(({ adapter }: { adapter: CanvasEntityAdapter }) => {
           loadingText={t('controlLayers.reset')}
           variant="ghost"
         >
-          {t('controlLayers.transform.reset')}
+          {t('accessibility.reset')}
         </Button>
+        <Spacer />
         <Button
           leftIcon={<PiCheckBold />}
           onClick={adapter.transformer.applyTransform}
@@ -53,7 +50,7 @@ const TransformBox = memo(({ adapter }: { adapter: CanvasEntityAdapter }) => {
           loadingText={t('common.apply')}
           variant="ghost"
         >
-          {t('controlLayers.transform.apply')}
+          {t('common.apply')}
         </Button>
         <Button
           leftIcon={<PiXBold />}
@@ -62,7 +59,7 @@ const TransformBox = memo(({ adapter }: { adapter: CanvasEntityAdapter }) => {
           loadingText={t('common.cancel')}
           variant="ghost"
         >
-          {t('controlLayers.transform.cancel')}
+          {t('common.cancel')}
         </Button>
       </ButtonGroup>
     </Flex>
@@ -73,11 +70,15 @@ TransformBox.displayName = 'Transform';
 
 export const Transform = () => {
   const canvasManager = useCanvasManager();
-  const adapter = useStore(canvasManager.stateApi.$transformingAdapter);
+  const transformingEntity = useStore(canvasManager.stateApi.$transformingEntity);
 
-  if (!adapter) {
+  if (!transformingEntity) {
     return null;
   }
 
-  return <TransformBox adapter={adapter} />;
+  return (
+    <EntityIdentifierContext.Provider value={transformingEntity}>
+      <TransformBox />
+    </EntityIdentifierContext.Provider>
+  );
 };

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityGroupList.tsx

@@ -2,12 +2,11 @@ import type { SystemStyleObject } from '@invoke-ai/ui-library';
 import { Button, Collapse, Flex, Icon, Spacer, Text } from '@invoke-ai/ui-library';
 import { useBoolean } from 'common/hooks/useBoolean';
 import { CanvasEntityAddOfTypeButton } from 'features/controlLayers/components/common/CanvasEntityAddOfTypeButton';
-import { CanvasEntityMergeVisibleButton } from 'features/controlLayers/components/common/CanvasEntityMergeVisibleButton';
 import { CanvasEntityTypeIsHiddenToggle } from 'features/controlLayers/components/common/CanvasEntityTypeIsHiddenToggle';
 import { useEntityTypeTitle } from 'features/controlLayers/hooks/useEntityTypeTitle';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import type { PropsWithChildren } from 'react';
-import { memo, useMemo } from 'react';
+import { memo } from 'react';
 import { PiCaretDownBold } from 'react-icons/pi';
 
 type Props = PropsWithChildren<{
@@ -22,9 +21,6 @@ const _hover: SystemStyleObject = {
 export const CanvasEntityGroupList = memo(({ isSelected, type, children }: Props) => {
   const title = useEntityTypeTitle(type);
   const collapse = useBoolean(true);
-  const canMergeVisible = useMemo(() => type === 'raster_layer' || type === 'inpaint_mask', [type]);
-  const canHideAll = useMemo(() => type !== 'ip_adapter', [type]);
-
   return (
     <Flex flexDir="column" w="full">
       <Flex w="full">
@@ -58,9 +54,8 @@ export const CanvasEntityGroupList = memo(({ isSelected, type, children }: Props
           </Text>
           <Spacer />
         </Flex>
-        {canMergeVisible && <CanvasEntityMergeVisibleButton type={type} />}
         <CanvasEntityAddOfTypeButton type={type} />
-        {canHideAll && <CanvasEntityTypeIsHiddenToggle type={type} />}
+        {type !== 'ip_adapter' && <CanvasEntityTypeIsHiddenToggle type={type} />}
       </Flex>
       <Collapse in={collapse.isTrue}>
         <Flex flexDir="column" gap={2} pt={2}>

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityHeader.tsx

@@ -56,7 +56,7 @@ export const CanvasEntityHeader = memo(({ children, ...rest }: FlexProps) => {
   }, [entityIdentifier]);
 
   return (
-    <ContextMenu renderMenu={renderMenu}>
+    <ContextMenu renderMenu={renderMenu} stopImmediatePropagation>
       {(ref) => (
         <Flex ref={ref} gap={2} alignItems="center" p={2} {...rest}>
           {children}

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityHeaderCommonActions.tsx

@@ -1,7 +1,6 @@
 import { Flex } from '@invoke-ai/ui-library';
 import { CanvasEntityDeleteButton } from 'features/controlLayers/components/common/CanvasEntityDeleteButton';
 import { CanvasEntityEnabledToggle } from 'features/controlLayers/components/common/CanvasEntityEnabledToggle';
-import { CanvasEntityIsBookmarkedForQuickSwitchToggle } from 'features/controlLayers/components/common/CanvasEntityIsBookmarkedForQuickSwitchToggle';
 import { CanvasEntityIsLockedToggle } from 'features/controlLayers/components/common/CanvasEntityIsLockedToggle';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import { memo } from 'react';
@@ -11,7 +10,6 @@ export const CanvasEntityHeaderCommonActions = memo(() => {
 
   return (
     <Flex alignSelf="stretch">
-      <CanvasEntityIsBookmarkedForQuickSwitchToggle />
       {entityIdentifier.type !== 'ip_adapter' && <CanvasEntityIsLockedToggle />}
       <CanvasEntityEnabledToggle />
       <CanvasEntityDeleteButton />

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityIsBookmarkedForQuickSwitchToggle.tsx

@@ -1,36 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useEntityIsBookmarkedForQuickSwitch } from 'features/controlLayers/hooks/useEntityIsBookmarkedForQuickSwitch';
-import { bookmarkedEntityChanged } from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiBookmarkSimpleBold, PiBookmarkSimpleFill } from 'react-icons/pi';
-
-export const CanvasEntityIsBookmarkedForQuickSwitchToggle = memo(() => {
-  const { t } = useTranslation();
-  const entityIdentifier = useEntityIdentifierContext();
-  const isBookmarked = useEntityIsBookmarkedForQuickSwitch(entityIdentifier);
-  const dispatch = useAppDispatch();
-  const onClick = useCallback(() => {
-    if (isBookmarked) {
-      dispatch(bookmarkedEntityChanged({ entityIdentifier: null }));
-    } else {
-      dispatch(bookmarkedEntityChanged({ entityIdentifier }));
-    }
-  }, [dispatch, entityIdentifier, isBookmarked]);
-
-  return (
-    <IconButton
-      size="sm"
-      aria-label={t(isBookmarked ? 'controlLayers.removeBookmark' : 'controlLayers.bookmark')}
-      tooltip={t(isBookmarked ? 'controlLayers.removeBookmark' : 'controlLayers.bookmark')}
-      variant="link"
-      alignSelf="stretch"
-      icon={isBookmarked ? <PiBookmarkSimpleFill /> : <PiBookmarkSimpleBold />}
-      onClick={onClick}
-    />
-  );
-});
-
-CanvasEntityIsBookmarkedForQuickSwitchToggle.displayName = 'CanvasEntityIsBookmarkedForQuickSwitchToggle';

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityMenuItemsArrange.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 {
   entityArrangedBackwardOne,
   entityArrangedForwardOne,
@@ -56,7 +55,6 @@ export const CanvasEntityMenuItemsArrange = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const entityIdentifier = useEntityIdentifierContext();
-  const isBusy = useCanvasIsBusy();
   const selectValidActions = useMemo(
     () =>
       createMemoizedSelector(selectCanvasSlice, (canvas) => {
@@ -88,24 +86,16 @@ export const CanvasEntityMenuItemsArrange = memo(() => {
 
   return (
     <>
-      <MenuItem onClick={moveToFront} isDisabled={!validActions.canMoveToFront || isBusy} icon={<PiArrowLineUpBold />}>
+      <MenuItem onClick={moveToFront} isDisabled={!validActions.canMoveToFront} icon={<PiArrowLineUpBold />}>
         {t('controlLayers.moveToFront')}
       </MenuItem>
-      <MenuItem
-        onClick={moveForwardOne}
-        isDisabled={!validActions.canMoveForwardOne || isBusy}
-        icon={<PiArrowUpBold />}
-      >
+      <MenuItem onClick={moveForwardOne} isDisabled={!validActions.canMoveForwardOne} icon={<PiArrowUpBold />}>
         {t('controlLayers.moveForward')}
       </MenuItem>
-      <MenuItem
-        onClick={moveBackwardOne}
-        isDisabled={!validActions.canMoveBackwardOne || isBusy}
-        icon={<PiArrowDownBold />}
-      >
+      <MenuItem onClick={moveBackwardOne} isDisabled={!validActions.canMoveBackwardOne} icon={<PiArrowDownBold />}>
         {t('controlLayers.moveBackward')}
       </MenuItem>
-      <MenuItem onClick={moveToBack} isDisabled={!validActions.canMoveToBack || isBusy} icon={<PiArrowLineDownBold />}>
+      <MenuItem onClick={moveToBack} isDisabled={!validActions.canMoveToBack} icon={<PiArrowLineDownBold />}>
         {t('controlLayers.moveToBack')}
       </MenuItem>
     </>

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityMenuItemsDelete.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 { entityDeleted } from 'features/controlLayers/store/canvasSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -11,14 +10,13 @@ export const CanvasEntityMenuItemsDelete = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const entityIdentifier = useEntityIdentifierContext();
-  const isBusy = useCanvasIsBusy();
 
   const deleteEntity = useCallback(() => {
     dispatch(entityDeleted({ entityIdentifier }));
   }, [dispatch, entityIdentifier]);
 
   return (
-    <MenuItem onClick={deleteEntity} icon={<PiTrashSimpleBold />} isDestructive isDisabled={isBusy}>
+    <MenuItem onClick={deleteEntity} icon={<PiTrashSimpleBold />} color="error.300">
       {t('common.delete')}
     </MenuItem>
   );

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityMenuItemsDuplicate.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 { entityDuplicated } from 'features/controlLayers/store/canvasSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -11,14 +10,13 @@ export const CanvasEntityMenuItemsDuplicate = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
   const entityIdentifier = useEntityIdentifierContext();
-  const isBusy = useCanvasIsBusy();
 
   const onClick = useCallback(() => {
     dispatch(entityDuplicated({ entityIdentifier }));
   }, [dispatch, entityIdentifier]);
 
   return (
-    <MenuItem onClick={onClick} icon={<PiCopyFill />} isDisabled={isBusy}>
+    <MenuItem onClick={onClick} icon={<PiCopyFill />}>
       {t('controlLayers.duplicate')}
     </MenuItem>
   );

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityMenuItemsFilter.tsx

@@ -1,7 +1,6 @@
 import { MenuItem } from '@invoke-ai/ui-library';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiShootingStarBold } from 'react-icons/pi';
@@ -10,14 +9,13 @@ export const CanvasEntityMenuItemsFilter = memo(() => {
   const { t } = useTranslation();
   const canvasManager = useCanvasManager();
   const entityIdentifier = useEntityIdentifierContext();
-  const isBusy = useCanvasIsBusy();
 
   const onClick = useCallback(() => {
-    canvasManager.filter.startFilter(entityIdentifier);
-  }, [canvasManager.filter, entityIdentifier]);
+    canvasManager.filter.initialize(entityIdentifier);
+  }, [entityIdentifier, canvasManager.filter]);
 
   return (
-    <MenuItem onClick={onClick} icon={<PiShootingStarBold />} isDisabled={isBusy}>
+    <MenuItem onClick={onClick} icon={<PiShootingStarBold />}>
       {t('controlLayers.filter.filter')}
     </MenuItem>
   );

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityMenuItemsTransform.tsx

@@ -1,6 +1,7 @@
 import { MenuItem } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import { useEntityAdapter } from 'features/controlLayers/hooks/useEntityAdapter';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -9,16 +10,17 @@ import { PiFrameCornersBold } from 'react-icons/pi';
 export const CanvasEntityMenuItemsTransform = memo(() => {
   const { t } = useTranslation();
   const entityIdentifier = useEntityIdentifierContext();
+  const canvasManager = useCanvasManager();
   const adapter = useEntityAdapter(entityIdentifier);
-  const isBusy = useCanvasIsBusy();
+  const transformingEntity = useStore(canvasManager.stateApi.$transformingEntity);
 
   const onClick = useCallback(() => {
     adapter.transformer.startTransform();
   }, [adapter.transformer]);
 
   return (
-    <MenuItem onClick={onClick} icon={<PiFrameCornersBold />} isDisabled={isBusy}>
-      {t('controlLayers.transform.transform')}
+    <MenuItem onClick={onClick} icon={<PiFrameCornersBold />} isDisabled={Boolean(transformingEntity)}>
+      {t('controlLayers.tool.transform')}
     </MenuItem>
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityMergeVisibleButton.tsx

@@ -1,91 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { logger } from 'app/logging/logger';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { isOk, withResultAsync } from 'common/util/result';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { useEntityTypeCount } from 'features/controlLayers/hooks/useEntityTypeCount';
-import { inpaintMaskAdded, rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
-import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
-import { imageDTOToImageObject } from 'features/controlLayers/store/types';
-import { toast } from 'features/toast/toast';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiStackBold } from 'react-icons/pi';
-import { serializeError } from 'serialize-error';
-
-const log = logger('canvas');
-
-type Props = {
-  type: CanvasEntityIdentifier['type'];
-};
-
-export const CanvasEntityMergeVisibleButton = memo(({ type }: Props) => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const canvasManager = useCanvasManager();
-  const entityCount = useEntityTypeCount(type);
-  const onClick = useCallback(async () => {
-    if (type === 'raster_layer') {
-      const rect = canvasManager.stage.getVisibleRect('raster_layer');
-      const result = await withResultAsync(() =>
-        canvasManager.compositor.rasterizeAndUploadCompositeRasterLayer(rect, false)
-      );
-
-      if (isOk(result)) {
-        dispatch(
-          rasterLayerAdded({
-            isSelected: true,
-            overrides: {
-              objects: [imageDTOToImageObject(result.value)],
-              position: { x: Math.floor(rect.x), y: Math.floor(rect.y) },
-            },
-            deleteOthers: true,
-          })
-        );
-        toast({ title: t('controlLayers.mergeVisibleOk') });
-      } else {
-        log.error({ error: serializeError(result.error) }, 'Failed to merge visible');
-        toast({ title: t('controlLayers.mergeVisibleError'), status: 'error' });
-      }
-    } else if (type === 'inpaint_mask') {
-      const rect = canvasManager.stage.getVisibleRect('inpaint_mask');
-      const result = await withResultAsync(() =>
-        canvasManager.compositor.rasterizeAndUploadCompositeInpaintMask(rect, false)
-      );
-
-      if (isOk(result)) {
-        dispatch(
-          inpaintMaskAdded({
-            isSelected: true,
-            overrides: {
-              objects: [imageDTOToImageObject(result.value)],
-              position: { x: Math.floor(rect.x), y: Math.floor(rect.y) },
-            },
-            deleteOthers: true,
-          })
-        );
-        toast({ title: t('controlLayers.mergeVisibleOk') });
-      } else {
-        log.error({ error: serializeError(result.error) }, 'Failed to merge visible');
-        toast({ title: t('controlLayers.mergeVisibleError'), status: 'error' });
-      }
-    } else {
-      log.error({ type }, 'Unsupported type for merge visible');
-    }
-  }, [canvasManager.compositor, canvasManager.stage, dispatch, t, type]);
-
-  return (
-    <IconButton
-      size="sm"
-      aria-label={t('controlLayers.mergeVisible')}
-      tooltip={t('controlLayers.mergeVisible')}
-      variant="link"
-      icon={<PiStackBold />}
-      onClick={onClick}
-      alignSelf="stretch"
-      isDisabled={entityCount <= 1}
-    />
-  );
-});
-
-CanvasEntityMergeVisibleButton.displayName = 'CanvasEntityMergeVisibleButton';

invokeai/frontend/web/src/features/controlLayers/components/common/CanvasEntityPreviewImage.tsx

@@ -4,7 +4,7 @@ import { createSelector } from '@reduxjs/toolkit';
 import { rgbColorToString } from 'common/util/colorCodeTransformers';
 import { useEntityAdapter } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { TRANSPARENCY_CHECKERBOARD_PATTERN_DATAURL } from 'features/controlLayers/konva/patterns/transparency-checkerboard-pattern';
+import { TRANSPARENCY_CHECKER_PATTERN } from 'features/controlLayers/konva/constants';
 import { selectCanvasSlice, selectEntity } from 'features/controlLayers/store/selectors';
 import { memo, useEffect, useMemo, useRef } from 'react';
 import { useSelector } from 'react-redux';
@@ -69,7 +69,7 @@ export const CanvasEntityPreviewImage = memo(() => {
       ctx.globalCompositeOperation = 'source-in';
       ctx.fillRect(0, 0, rect.width, rect.height);
     }
-  }, [cache, maskColor]);
+  }, [adapter.transformer, adapter.transformer.nodeRect, adapter.transformer.pixelRect, cache, maskColor]);
 
   return (
     <Flex
@@ -88,7 +88,7 @@ export const CanvasEntityPreviewImage = memo(() => {
         right={0}
         bottom={0}
         left={0}
-        bgImage={TRANSPARENCY_CHECKERBOARD_PATTERN_DATAURL}
+        bgImage={TRANSPARENCY_CHECKER_PATTERN}
         bgSize="5px"
         opacity={0.1}
       />

invokeai/frontend/web/src/features/controlLayers/contexts/EntityAdapterContext.tsx

@@ -1,28 +1,27 @@
+import type { SyncableMap } from 'common/util/SyncableMap/SyncableMap';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import type { CanvasEntityAdapterControlLayer } from 'features/controlLayers/konva/CanvasEntityAdapter/CanvasEntityAdapterControlLayer';
-import type { CanvasEntityAdapterInpaintMask } from 'features/controlLayers/konva/CanvasEntityAdapter/CanvasEntityAdapterInpaintMask';
-import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konva/CanvasEntityAdapter/CanvasEntityAdapterRasterLayer';
-import type { CanvasEntityAdapterRegionalGuidance } from 'features/controlLayers/konva/CanvasEntityAdapter/CanvasEntityAdapterRegionalGuidance';
+import type { CanvasEntityLayerAdapter } from 'features/controlLayers/konva/CanvasEntityLayerAdapter';
+import type { CanvasEntityMaskAdapter } from 'features/controlLayers/konva/CanvasEntityMaskAdapter';
 import type { PropsWithChildren } from 'react';
 import { createContext, memo, useContext, useMemo, useSyncExternalStore } from 'react';
 import { assert } from 'tsafe';
 
-const EntityAdapterContext = createContext<
-  | CanvasEntityAdapterRasterLayer
-  | CanvasEntityAdapterControlLayer
-  | CanvasEntityAdapterInpaintMask
-  | CanvasEntityAdapterRegionalGuidance
-  | null
->(null);
+const EntityAdapterContext = createContext<CanvasEntityLayerAdapter | CanvasEntityMaskAdapter | null>(null);
 
-export const RasterLayerAdapterGate = memo(({ children }: PropsWithChildren) => {
+export const EntityLayerAdapterGate = memo(({ children }: PropsWithChildren) => {
   const canvasManager = useCanvasManager();
   const entityIdentifier = useEntityIdentifierContext();
-  const adapters = useSyncExternalStore(
-    canvasManager.adapters.rasterLayers.subscribe,
-    canvasManager.adapters.rasterLayers.getSnapshot
-  );
+  const store = useMemo<SyncableMap<string, CanvasEntityLayerAdapter>>(() => {
+    if (entityIdentifier.type === 'raster_layer') {
+      return canvasManager.adapters.rasterLayers;
+    }
+    if (entityIdentifier.type === 'control_layer') {
+      return canvasManager.adapters.controlLayers;
+    }
+    assert(false, 'Unknown entity type');
+  }, [canvasManager.adapters.controlLayers, canvasManager.adapters.rasterLayers, entityIdentifier.type]);
+  const adapters = useSyncExternalStore(store.subscribe, store.getSnapshot);
   const adapter = useMemo(() => {
     return adapters.get(entityIdentifier.id) ?? null;
   }, [adapters, entityIdentifier.id]);
@@ -34,15 +33,28 @@ export const RasterLayerAdapterGate = memo(({ children }: PropsWithChildren) =>
   return <EntityAdapterContext.Provider value={adapter}>{children}</EntityAdapterContext.Provider>;
 });
 
-RasterLayerAdapterGate.displayName = 'RasterLayerAdapterGate';
+EntityLayerAdapterGate.displayName = 'EntityLayerAdapterGate';
 
-export const ControlLayerAdapterGate = memo(({ children }: PropsWithChildren) => {
+// export const useEntityLayerAdapter = (): CanvasLayerAdapter => {
+//   const adapter = useContext(EntityAdapterContext);
+//   assert(adapter, 'useEntityLayerAdapter must be used within a EntityLayerAdapterGate');
+//   assert(adapter.type === 'layer_adapter', 'useEntityLayerAdapter must be used with a layer adapter');
+//   return adapter;
+// };
+
+export const EntityMaskAdapterGate = memo(({ children }: PropsWithChildren) => {
   const canvasManager = useCanvasManager();
   const entityIdentifier = useEntityIdentifierContext();
-  const adapters = useSyncExternalStore(
-    canvasManager.adapters.controlLayers.subscribe,
-    canvasManager.adapters.controlLayers.getSnapshot
-  );
+  const store = useMemo<SyncableMap<string, CanvasEntityMaskAdapter>>(() => {
+    if (entityIdentifier.type === 'inpaint_mask') {
+      return canvasManager.adapters.inpaintMasks;
+    }
+    if (entityIdentifier.type === 'regional_guidance') {
+      return canvasManager.adapters.regionMasks;
+    }
+    assert(false, 'Unknown entity type');
+  }, [canvasManager.adapters.inpaintMasks, canvasManager.adapters.regionMasks, entityIdentifier.type]);
+  const adapters = useSyncExternalStore(store.subscribe, store.getSnapshot);
   const adapter = useMemo(() => {
     return adapters.get(entityIdentifier.id) ?? null;
   }, [adapters, entityIdentifier.id]);
@@ -54,53 +66,16 @@ export const ControlLayerAdapterGate = memo(({ children }: PropsWithChildren) =>
   return <EntityAdapterContext.Provider value={adapter}>{children}</EntityAdapterContext.Provider>;
 });
 
-ControlLayerAdapterGate.displayName = 'ControlLayerAdapterGate';
+EntityMaskAdapterGate.displayName = 'EntityMaskAdapterGate';
 
-export const InpaintMaskAdapterGate = memo(({ children }: PropsWithChildren) => {
-  const canvasManager = useCanvasManager();
-  const entityIdentifier = useEntityIdentifierContext();
-  const adapters = useSyncExternalStore(
-    canvasManager.adapters.inpaintMasks.subscribe,
-    canvasManager.adapters.inpaintMasks.getSnapshot
-  );
-  const adapter = useMemo(() => {
-    return adapters.get(entityIdentifier.id) ?? null;
-  }, [adapters, entityIdentifier.id]);
+// export const useEntityMaskAdapter = (): CanvasMaskAdapter => {
+//   const adapter = useContext(EntityAdapterContext);
+//   assert(adapter, 'useEntityMaskAdapter must be used within a CanvasMaskAdapterGate');
+//   assert(adapter.type === 'mask_adapter', 'useEntityMaskAdapter must be used with a mask adapter');
+//   return adapter;
+// };
 
-  if (!adapter) {
-    return null;
-  }
-
-  return <EntityAdapterContext.Provider value={adapter}>{children}</EntityAdapterContext.Provider>;
-});
-
-InpaintMaskAdapterGate.displayName = 'InpaintMaskAdapterGate';
-
-export const RegionalGuidanceAdapterGate = memo(({ children }: PropsWithChildren) => {
-  const canvasManager = useCanvasManager();
-  const entityIdentifier = useEntityIdentifierContext();
-  const adapters = useSyncExternalStore(
-    canvasManager.adapters.regionMasks.subscribe,
-    canvasManager.adapters.regionMasks.getSnapshot
-  );
-  const adapter = useMemo(() => {
-    return adapters.get(entityIdentifier.id) ?? null;
-  }, [adapters, entityIdentifier.id]);
-
-  if (!adapter) {
-    return null;
-  }
-
-  return <EntityAdapterContext.Provider value={adapter}>{children}</EntityAdapterContext.Provider>;
-});
-
-RegionalGuidanceAdapterGate.displayName = 'RegionalGuidanceAdapterGate';
-
-export const useEntityAdapter = ():
-  | CanvasEntityAdapterRasterLayer
-  | CanvasEntityAdapterControlLayer
-  | CanvasEntityAdapterInpaintMask
-  | CanvasEntityAdapterRegionalGuidance => {
+export const useEntityAdapter = (): CanvasEntityLayerAdapter | CanvasEntityMaskAdapter => {
   const adapter = useContext(EntityAdapterContext);
   assert(adapter, 'useEntityAdapter must be used within a CanvasRasterLayerAdapterGate');
   return adapter;

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

@@ -1,47 +0,0 @@
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { entitySelected } from 'features/controlLayers/store/canvasSlice';
-import {
-  selectBookmarkedEntityIdentifier,
-  selectSelectedEntityIdentifier,
-} from 'features/controlLayers/store/selectors';
-import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
-import { useCallback, useEffect, useState } from 'react';
-import { useHotkeys } from 'react-hotkeys-hook';
-
-export const useCanvasEntityQuickSwitchHotkey = () => {
-  const dispatch = useAppDispatch();
-  const [prev, setPrev] = useState<CanvasEntityIdentifier | null>(null);
-  const [current, setCurrent] = useState<CanvasEntityIdentifier | null>(null);
-  const selected = useAppSelector(selectSelectedEntityIdentifier);
-  const bookmarked = useAppSelector(selectBookmarkedEntityIdentifier);
-
-  // Update prev and current when selected entity changes
-  useEffect(() => {
-    if (current?.id !== selected?.id) {
-      setPrev(current);
-      setCurrent(selected);
-    }
-  }, [current, selected]);
-
-  const onQuickSwitch = useCallback(() => {
-    if (bookmarked) {
-      if (current?.id !== bookmarked.id) {
-        // Switch between current (non-bookmarked) and bookmarked
-        setPrev(current);
-        setCurrent(bookmarked);
-        dispatch(entitySelected({ entityIdentifier: bookmarked }));
-      } else if (prev) {
-        // Switch back to the last non-bookmarked entity
-        setCurrent(prev);
-        dispatch(entitySelected({ entityIdentifier: prev }));
-      }
-    } else if (prev !== null && current !== null) {
-      // Switch between prev and current if no bookmarked entity
-      setPrev(current);
-      setCurrent(prev);
-      dispatch(entitySelected({ entityIdentifier: prev }));
-    }
-  }, [bookmarked, current, dispatch, prev]);
-
-  useHotkeys('q', onQuickSwitch);
-};

invokeai/frontend/web/src/features/controlLayers/hooks/useCanvasUndoRedo.tsx

@@ -1,3 +1,4 @@
+/* eslint-disable i18next/no-literal-string */
 import { useAppSelector } from 'app/store/storeHooks';
 import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
 import { canvasRedo, canvasUndo } from 'features/controlLayers/store/canvasSlice';
@@ -6,7 +7,7 @@ import { useCallback } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useDispatch } from 'react-redux';
 
-export const useCanvasUndoRedoHotkeys = () => {
+export const useCanvasUndoRedo = () => {
   useAssertSingleton('useCanvasUndoRedo');
   const dispatch = useDispatch();
 

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

@@ -1,26 +1,20 @@
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import type { CanvasEntityAdapterControlLayer } from 'features/controlLayers/konva/CanvasEntityAdapter/CanvasEntityAdapterControlLayer';
-import type { CanvasEntityAdapterInpaintMask } from 'features/controlLayers/konva/CanvasEntityAdapter/CanvasEntityAdapterInpaintMask';
-import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konva/CanvasEntityAdapter/CanvasEntityAdapterRasterLayer';
-import type { CanvasEntityAdapterRegionalGuidance } from 'features/controlLayers/konva/CanvasEntityAdapter/CanvasEntityAdapterRegionalGuidance';
+import type { CanvasEntityLayerAdapter } from 'features/controlLayers/konva/CanvasEntityLayerAdapter';
+import type { CanvasEntityMaskAdapter } from 'features/controlLayers/konva/CanvasEntityMaskAdapter';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { useMemo } from 'react';
 import { assert } from 'tsafe';
 
 export const useEntityAdapter = (
   entityIdentifier: CanvasEntityIdentifier
-):
-  | CanvasEntityAdapterRasterLayer
-  | CanvasEntityAdapterControlLayer
-  | CanvasEntityAdapterInpaintMask
-  | CanvasEntityAdapterRegionalGuidance => {
+): CanvasEntityLayerAdapter | CanvasEntityMaskAdapter => {
   const canvasManager = useCanvasManager();
 
   const adapter = useMemo(() => {
-    const adapter = canvasManager.getAdapter(entityIdentifier);
-    assert(adapter, 'Entity adapter not found');
-    return adapter;
-  }, [canvasManager, entityIdentifier]);
+    const entity = canvasManager.stateApi.getEntity(entityIdentifier);
+    assert(entity, 'Entity adapter not found');
+    return entity.adapter;
+  }, [canvasManager.stateApi, entityIdentifier]);
 
   return adapter;
 };

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

@@ -1,18 +0,0 @@
-import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/store/storeHooks';
-import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
-import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
-import { useMemo } from 'react';
-
-export const useEntityIsBookmarkedForQuickSwitch = (entityIdentifier: CanvasEntityIdentifier) => {
-  const selectIsBookmarkedForQuickSwitch = useMemo(
-    () =>
-      createSelector(selectCanvasSlice, (canvas) => {
-        return canvas.bookmarkedEntityIdentifier?.id === entityIdentifier.id;
-      }),
-    [entityIdentifier]
-  );
-  const isBookmarkedForQuickSwitch = useAppSelector(selectIsBookmarkedForQuickSwitch);
-
-  return isBookmarkedForQuickSwitch;
-};