Run ruff, fix bug in hf downloading code which failed to download parts of a model

docs/contributing/dev-environment.md

@@ -17,49 +17,46 @@ If you just want to use Invoke, you should use the [installer][installer link].
 ## Setup
 
 1. Run through the [requirements][requirements link].
-2. [Fork and clone][forking link] the [InvokeAI repo][repo link].
-3. Create an directory for user data (images, models, db, etc). This is typically at `~/invokeai`, but if you already have a non-dev install, you may want to create a separate directory for the dev install.
-4. Create a python virtual environment inside the directory you just created:
+1. [Fork and clone][forking link] the [InvokeAI repo][repo link].
+1. Create an directory for user data (images, models, db, etc). This is typically at `~/invokeai`, but if you already have a non-dev install, you may want to create a separate directory for the dev install.
+1. Create a python virtual environment inside the directory you just created:
 
-      ```sh
-      python3 -m venv .venv --prompt InvokeAI-Dev
-      ```
+   ```sh
+   python3 -m venv .venv --prompt InvokeAI-Dev
+   ```
 
-5. Activate the venv (you'll need to do this every time you want to run the app):
+1. Activate the venv (you'll need to do this every time you want to run the app):
 
-        ```sh
-        source .venv/bin/activate
-        ```
+   ```sh
+   source .venv/bin/activate
+   ```
 
-6. Install the repo as an [editable install][editable install link]:
+1. Install the repo as an [editable install][editable install link]:
 
-      ```sh
-      pip install -e ".[dev,test,xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
-      ```
+   ```sh
+   pip install -e ".[dev,test,xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+   ```
 
-      Refer to the [manual installation][manual install link]] instructions for more determining the correct install options. `xformers` is optional, but `dev` and `test` are not.
+   Refer to the [manual installation][manual install link]] instructions for more determining the correct install options. `xformers` is optional, but `dev` and `test` are not.
 
-7. Install the frontend dev toolchain:
+1. Install the frontend dev toolchain:
 
    - [`nodejs`](https://nodejs.org/) (recommend v20 LTS)
-   - [`pnpm`](https://pnpm.io/8.x/installation) (must be v8 - not v9!)
+   - [`pnpm`](https://pnpm.io/installation#installing-a-specific-version) (must be v8 - not v9!)
 
-8. Do a production build of the frontend:
+1. Do a production build of the frontend:
 
-      ```sh
-      cd PATH_TO_INVOKEAI_REPO/invokeai/frontend/web
-      pnpm i
-      pnpm build
-      ```
+   ```sh
+   pnpm build
+   ```
 
-9. Start the application:
+1. Start the application:
 
-      ```sh
-      cd PATH_TO_INVOKEAI_REPO
-      python scripts/invokeai-web.py
-      ```
+   ```sh
+   python scripts/invokeai-web.py
+   ```
 
-10. Access the UI at `localhost:9090`.
+1. Access the UI at `localhost:9090`.
 
 ## Updating the UI
 

invokeai/app/api/routers/model_manager.py

@@ -808,11 +808,7 @@ def get_is_installed(
     for model in installed_models:
         if model.source == starter_model.source:
             return True
-        if (
-            (model.name == starter_model.name or model.name in starter_model.previous_names)
-            and model.base == starter_model.base
-            and model.type == starter_model.type
-        ):
+        if model.name == starter_model.name and model.base == starter_model.base and model.type == starter_model.type:
             return True
     return False
 

invokeai/app/invocations/denoise_latents.py

@@ -13,7 +13,6 @@ from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from diffusers.schedulers.scheduling_dpmsolver_sde import DPMSolverSDEScheduler
 from diffusers.schedulers.scheduling_tcd import TCDScheduler
 from diffusers.schedulers.scheduling_utils import SchedulerMixin as Scheduler
-from PIL import Image
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPVisionModelWithProjection
@@ -511,7 +510,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         context: InvocationContext,
         t2i_adapters: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
         ext_manager: ExtensionsManager,
-        bgr_mode: bool = False,
     ) -> None:
         if t2i_adapters is None:
             return
@@ -521,10 +519,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             t2i_adapters = [t2i_adapters]
 
         for t2i_adapter_field in t2i_adapters:
-            image = context.images.get_pil(t2i_adapter_field.image.image_name)
-            if bgr_mode:  # SDXL t2i trained on cv2's BGR outputs, but PIL won't convert straight to BGR
-                r, g, b = image.split()
-                image = Image.merge("RGB", (b, g, r))
             ext_manager.add_extension(
                 T2IAdapterExt(
                     node_context=context,
@@ -629,10 +623,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 max_unet_downscale = 8
             elif t2i_adapter_model_config.base == BaseModelType.StableDiffusionXL:
                 max_unet_downscale = 4
-
-                # SDXL adapters are trained on cv2's BGR outputs
-                r, g, b = image.split()
-                image = Image.merge("RGB", (b, g, r))
             else:
                 raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_config.base}'.")
 
@@ -910,8 +900,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             #    ext = extension_field.to_extension(exit_stack, context, ext_manager)
             #    ext_manager.add_extension(ext)
             self.parse_controlnet_field(exit_stack, context, self.control, ext_manager)
-            bgr_mode = self.unet.unet.base == BaseModelType.StableDiffusionXL
-            self.parse_t2i_adapter_field(exit_stack, context, self.t2i_adapter, ext_manager, bgr_mode)
+            self.parse_t2i_adapter_field(exit_stack, context, self.t2i_adapter, ext_manager)
 
             # ext: t2i/ip adapter
             ext_manager.run_callback(ExtensionCallbackType.SETUP, denoise_ctx)

invokeai/app/invocations/fields.py

@@ -41,6 +41,7 @@ class UIType(str, Enum, metaclass=MetaEnum):
     # region Model Field Types
     MainModel = "MainModelField"
     FluxMainModel = "FluxMainModelField"
+    SD3MainModel = "SD3MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
@@ -133,6 +134,7 @@ class FieldDescriptions:
     clip_embed_model = "CLIP Embed loader"
     unet = "UNet (scheduler, LoRAs)"
     transformer = "Transformer"
+    mmditx = "MMDiTX"
     vae = "VAE"
     cond = "Conditioning tensor"
     controlnet_model = "ControlNet model to load"
@@ -140,6 +142,7 @@ class FieldDescriptions:
     lora_model = "LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
     flux_model = "Flux model (Transformer) to load"
+    sd3_model = "SD3 model (MMDiTX) to load"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
@@ -246,6 +249,12 @@ class FluxConditioningField(BaseModel):
     conditioning_name: str = Field(description="The name of conditioning tensor")
 
 
+class SD3ConditioningField(BaseModel):
+    """A conditioning tensor primitive value"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+
+
 class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/flux_model_loader.py

@@ -0,0 +1,89 @@
+from typing import Literal
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.util import max_seq_lengths
+from invokeai.backend.model_manager.config import (
+    CheckpointConfigBase,
+    SubModelType,
+)
+
+
+@invocation_output("flux_model_loader_output")
+class FluxModelLoaderOutput(BaseInvocationOutput):
+    """Flux base model loader output"""
+
+    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
+    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+    max_seq_len: Literal[256, 512] = OutputField(
+        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
+        title="Max Seq Length",
+    )
+
+
+@invocation(
+    "flux_model_loader",
+    title="Flux Main Model",
+    tags=["model", "flux"],
+    category="model",
+    version="1.0.4",
+    classification=Classification.Prototype,
+)
+class FluxModelLoaderInvocation(BaseInvocation):
+    """Loads a flux base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.flux_model,
+        ui_type=UIType.FluxMainModel,
+        input=Input.Direct,
+    )
+
+    t5_encoder_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
+    )
+
+    clip_embed_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.clip_embed_model,
+        ui_type=UIType.CLIPEmbedModel,
+        input=Input.Direct,
+        title="CLIP Embed",
+    )
+
+    vae_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
+    )
+
+    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
+        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
+            if not context.models.exists(key):
+                raise ValueError(f"Unknown model: {key}")
+
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
+
+        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+
+        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+
+        transformer_config = context.models.get_config(transformer)
+        assert isinstance(transformer_config, CheckpointConfigBase)
+
+        return FluxModelLoaderOutput(
+            transformer=TransformerField(transformer=transformer, loras=[]),
+            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
+            vae=VAEField(vae=vae),
+            max_seq_len=max_seq_lengths[transformer_config.config_path],
+        )

invokeai/app/invocations/mask.py

@@ -165,7 +165,6 @@ class ApplyMaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     mask: TensorField = InputField(description="The mask tensor to apply.")
     image: ImageField = InputField(description="The image to apply the mask to.")
-    invert: bool = InputField(default=False, description="Whether to invert the mask.")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.images.get_pil(self.image.image_name, mode="RGBA")
@@ -180,9 +179,6 @@ class ApplyMaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
             mask = mask > 0.5
         mask_np = (mask.float() * 255).byte().cpu().numpy().astype(np.uint8)
 
-        if self.invert:
-            mask_np = 255 - mask_np
-
         # Apply the mask only to the alpha channel where the original alpha is non-zero. This preserves the original
         # image's transparency - else the transparent regions would end up as opaque black.
 

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Literal, Optional
+from typing import List, Optional
 
 from pydantic import BaseModel, Field
 
@@ -13,11 +13,9 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.flux.util import max_seq_lengths
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
-    CheckpointConfigBase,
     ModelType,
     SubModelType,
 )
@@ -139,78 +137,6 @@ class ModelIdentifierInvocation(BaseInvocation):
         return ModelIdentifierOutput(model=self.model)
 
 
-@invocation_output("flux_model_loader_output")
-class FluxModelLoaderOutput(BaseInvocationOutput):
-    """Flux base model loader output"""
-
-    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
-    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
-    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
-    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
-    max_seq_len: Literal[256, 512] = OutputField(
-        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
-        title="Max Seq Length",
-    )
-
-
-@invocation(
-    "flux_model_loader",
-    title="Flux Main Model",
-    tags=["model", "flux"],
-    category="model",
-    version="1.0.4",
-    classification=Classification.Prototype,
-)
-class FluxModelLoaderInvocation(BaseInvocation):
-    """Loads a flux base model, outputting its submodels."""
-
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.flux_model,
-        ui_type=UIType.FluxMainModel,
-        input=Input.Direct,
-    )
-
-    t5_encoder_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
-    )
-
-    clip_embed_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.clip_embed_model,
-        ui_type=UIType.CLIPEmbedModel,
-        input=Input.Direct,
-        title="CLIP Embed",
-    )
-
-    vae_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
-    )
-
-    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
-        for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
-            if not context.models.exists(key):
-                raise ValueError(f"Unknown model: {key}")
-
-        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
-
-        tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-
-        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
-        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
-
-        transformer_config = context.models.get_config(transformer)
-        assert isinstance(transformer_config, CheckpointConfigBase)
-
-        return FluxModelLoaderOutput(
-            transformer=TransformerField(transformer=transformer, loras=[]),
-            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
-            vae=VAEField(vae=vae),
-            max_seq_len=max_seq_lengths[transformer_config.config_path],
-        )
-
-
 @invocation(
     "main_model_loader",
     title="Main Model",

invokeai/app/invocations/primitives.py

@@ -18,6 +18,7 @@ from invokeai.app.invocations.fields import (
     InputField,
     LatentsField,
     OutputField,
+    SD3ConditioningField,
     TensorField,
     UIComponent,
 )
@@ -426,6 +427,17 @@ class FluxConditioningOutput(BaseInvocationOutput):
         return cls(conditioning=FluxConditioningField(conditioning_name=conditioning_name))
 
 
+@invocation_output("sd3_conditioning_output")
+class SD3ConditioningOutput(BaseInvocationOutput):
+    """Base class for nodes that output a single SD3 conditioning tensor"""
+
+    conditioning: SD3ConditioningField = OutputField(description=FieldDescriptions.cond)
+
+    @classmethod
+    def build(cls, conditioning_name: str) -> "SD3ConditioningOutput":
+        return cls(conditioning=SD3ConditioningField(conditioning_name=conditioning_name))
+
+
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""

invokeai/app/invocations/sd3_denoise.py

@@ -0,0 +1,260 @@
+from typing import Callable, Tuple
+
+import torch
+from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
+from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
+from tqdm import tqdm
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    SD3ConditioningField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import TransformerField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.invocations.sd3_text_encoder import SD3_T5_MAX_SEQ_LEN
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import SD3ConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "sd3_denoise",
+    title="SD3 Denoise",
+    tags=["image", "sd3"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Run denoising process with a SD3 model."""
+
+    transformer: TransformerField = InputField(
+        description=FieldDescriptions.sd3_model,
+        input=Input.Connection,
+        title="Transformer",
+    )
+    positive_text_conditioning: SD3ConditioningField = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection
+    )
+    negative_text_conditioning: SD3ConditioningField = InputField(
+        description=FieldDescriptions.negative_cond, input=Input.Connection
+    )
+    cfg_scale: float | list[float] = InputField(default=3.5, description=FieldDescriptions.cfg_scale, title="CFG Scale")
+    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
+    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
+    num_steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
+    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = self._run_diffusion(context)
+        latents = latents.detach().to("cpu")
+
+        name = context.tensors.save(tensor=latents)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
+
+    def _load_text_conditioning(
+        self,
+        context: InvocationContext,
+        conditioning_name: str,
+        joint_attention_dim: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        sd3_conditioning = cond_data.conditionings[0]
+        assert isinstance(sd3_conditioning, SD3ConditioningInfo)
+        sd3_conditioning = sd3_conditioning.to(dtype=dtype, device=device)
+
+        t5_embeds = sd3_conditioning.t5_embeds
+        if t5_embeds is None:
+            t5_embeds = torch.zeros(
+                (1, SD3_T5_MAX_SEQ_LEN, joint_attention_dim),
+                device=device,
+                dtype=dtype,
+            )
+
+        clip_prompt_embeds = torch.cat([sd3_conditioning.clip_l_embeds, sd3_conditioning.clip_g_embeds], dim=-1)
+        clip_prompt_embeds = torch.nn.functional.pad(
+            clip_prompt_embeds, (0, t5_embeds.shape[-1] - clip_prompt_embeds.shape[-1])
+        )
+
+        prompt_embeds = torch.cat([clip_prompt_embeds, t5_embeds], dim=-2)
+        pooled_prompt_embeds = torch.cat(
+            [sd3_conditioning.clip_l_pooled_embeds, sd3_conditioning.clip_g_pooled_embeds], dim=-1
+        )
+
+        return prompt_embeds, pooled_prompt_embeds
+
+    def _get_noise(
+        self,
+        num_samples: int,
+        num_channels_latents: int,
+        height: int,
+        width: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        seed: int,
+    ) -> torch.Tensor:
+        # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
+        rand_device = "cpu"
+        rand_dtype = torch.float16
+
+        return torch.randn(
+            num_samples,
+            num_channels_latents,
+            int(height) // LATENT_SCALE_FACTOR,
+            int(width) // LATENT_SCALE_FACTOR,
+            device=rand_device,
+            dtype=rand_dtype,
+            generator=torch.Generator(device=rand_device).manual_seed(seed),
+        ).to(device=device, dtype=dtype)
+
+    def _prepare_cfg_scale(self, num_timesteps: int) -> list[float]:
+        """Prepare the CFG scale list.
+
+        Args:
+            num_timesteps (int): The number of timesteps in the scheduler. Could be different from num_steps depending
+            on the scheduler used (e.g. higher order schedulers).
+
+        Returns:
+            list[float]: _description_
+        """
+        if isinstance(self.cfg_scale, float):
+            cfg_scale = [self.cfg_scale] * num_timesteps
+        elif isinstance(self.cfg_scale, list):
+            assert len(self.cfg_scale) == num_timesteps
+            cfg_scale = self.cfg_scale
+        else:
+            raise ValueError(f"Invalid CFG scale type: {type(self.cfg_scale)}")
+
+        return cfg_scale
+
+    def _run_diffusion(
+        self,
+        context: InvocationContext,
+    ):
+        inference_dtype = TorchDevice.choose_torch_dtype()
+        device = TorchDevice.choose_torch_device()
+
+        transformer_info = context.models.load(self.transformer.transformer)
+
+        # Load/process the conditioning data.
+        # TODO(ryand): Make CFG optional.
+        do_classifier_free_guidance = True
+        pos_prompt_embeds, pos_pooled_prompt_embeds = self._load_text_conditioning(
+            context=context,
+            conditioning_name=self.positive_text_conditioning.conditioning_name,
+            joint_attention_dim=transformer_info.model.config.joint_attention_dim,
+            dtype=inference_dtype,
+            device=device,
+        )
+        neg_prompt_embeds, neg_pooled_prompt_embeds = self._load_text_conditioning(
+            context=context,
+            conditioning_name=self.negative_text_conditioning.conditioning_name,
+            joint_attention_dim=transformer_info.model.config.joint_attention_dim,
+            dtype=inference_dtype,
+            device=device,
+        )
+        # TODO(ryand): Support both sequential and batched CFG inference.
+        prompt_embeds = torch.cat([neg_prompt_embeds, pos_prompt_embeds], dim=0)
+        pooled_prompt_embeds = torch.cat([neg_pooled_prompt_embeds, pos_pooled_prompt_embeds], dim=0)
+
+        # Prepare the scheduler.
+        scheduler = FlowMatchEulerDiscreteScheduler()
+        scheduler.set_timesteps(num_inference_steps=self.num_steps, device=device)
+        timesteps = scheduler.timesteps
+        assert isinstance(timesteps, torch.Tensor)
+
+        # Prepare the CFG scale list.
+        cfg_scale = self._prepare_cfg_scale(len(timesteps))
+
+        # Generate initial latent noise.
+        num_channels_latents = transformer_info.model.config.in_channels
+        assert isinstance(num_channels_latents, int)
+        noise = self._get_noise(
+            num_samples=1,
+            num_channels_latents=num_channels_latents,
+            height=self.height,
+            width=self.width,
+            dtype=inference_dtype,
+            device=device,
+            seed=self.seed,
+        )
+        latents: torch.Tensor = noise
+
+        total_steps = len(timesteps)
+        step_callback = self._build_step_callback(context)
+
+        step_callback(
+            PipelineIntermediateState(
+                step=0,
+                order=1,
+                total_steps=total_steps,
+                timestep=int(timesteps[0]),
+                latents=latents,
+            ),
+        )
+
+        with transformer_info.model_on_device() as (cached_weights, transformer):
+            assert isinstance(transformer, SD3Transformer2DModel)
+
+            # 6. Denoising loop
+            for step_idx, t in tqdm(list(enumerate(timesteps))):
+                # Expand the latents if we are doing CFG.
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                # Expand the timestep to match the latent model input.
+                timestep = t.expand(latent_model_input.shape[0])
+
+                noise_pred = transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    pooled_projections=pooled_prompt_embeds,
+                    joint_attention_kwargs=None,
+                    return_dict=False,
+                )[0]
+
+                # Apply CFG.
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + cfg_scale[step_idx] * (noise_pred_cond - noise_pred_uncond)
+
+                # Compute the previous noisy sample x_t -> x_t-1.
+                latents_dtype = latents.dtype
+                latents = scheduler.step(model_output=noise_pred, timestep=t, sample=latents, return_dict=False)[0]
+
+                # TODO(ryand): This MPS dtype handling was copied from diffusers, I haven't tested to see if it's
+                # needed.
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                step_callback(
+                    PipelineIntermediateState(
+                        step=step_idx + 1,
+                        order=1,
+                        total_steps=total_steps,
+                        timestep=int(t),
+                        latents=latents,
+                    ),
+                )
+
+        return latents
+
+    def _build_step_callback(self, context: InvocationContext) -> Callable[[PipelineIntermediateState], None]:
+        def step_callback(state: PipelineIntermediateState) -> None:
+            context.util.sd_step_callback(state, BaseModelType.StableDiffusion3)
+
+        return step_callback

invokeai/app/invocations/sd3_model_loader.py

@@ -0,0 +1,97 @@
+from typing import Optional
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import SubModelType
+
+
+@invocation_output("sd3_model_loader_output")
+class Sd3ModelLoaderOutput(BaseInvocationOutput):
+    """SD3 base model loader output."""
+
+    mmditx: TransformerField = OutputField(description=FieldDescriptions.mmditx, title="MMDiTX")
+    clip_l: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP L")
+    clip_g: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP G")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+
+
+@invocation(
+    "sd3_model_loader",
+    title="SD3 Main Model",
+    tags=["model", "sd3"],
+    category="model",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class Sd3ModelLoaderInvocation(BaseInvocation):
+    """Loads a SD3 base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.sd3_model,
+        ui_type=UIType.SD3MainModel,
+        input=Input.Direct,
+    )
+
+    t5_encoder_model: Optional[ModelIdentifierField] = InputField(
+        description=FieldDescriptions.t5_encoder,
+        ui_type=UIType.T5EncoderModel,
+        input=Input.Direct,
+        title="T5 Encoder",
+        default=None,
+    )
+
+    # TODO(brandon): Setup UI updates to support selecting a clip l model.
+    # clip_l_model: ModelIdentifierField = InputField(
+    #     description=FieldDescriptions.clip_l_model,
+    #     ui_type=UIType.CLIPEmbedModel,
+    #     input=Input.Direct,
+    #     title="CLIP L Encoder",
+    # )
+
+    # TODO(brandon): Setup UI updates to support selecting a clip g model.
+    # clip_g_model: ModelIdentifierField = InputField(
+    #     description=FieldDescriptions.clip_g_model,
+    #     ui_type=UIType.CLIPGModel,
+    #     input=Input.Direct,
+    #     title="CLIP G Encoder",
+    # )
+
+    # TODO(brandon): Setup UI updates to support selecting an SD3 vae model.
+    # vae_model: ModelIdentifierField = InputField(
+    #     description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE", default=None
+    # )
+
+    def invoke(self, context: InvocationContext) -> Sd3ModelLoaderOutput:
+        mmditx = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+        tokenizer_l = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        clip_encoder_l = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        tokenizer_g = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        clip_encoder_g = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        tokenizer_t5 = (
+            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
+            if self.t5_encoder_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
+        )
+        t5_encoder = (
+            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
+            if self.t5_encoder_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
+        )
+
+        return Sd3ModelLoaderOutput(
+            mmditx=TransformerField(transformer=mmditx, loras=[]),
+            clip_l=CLIPField(tokenizer=tokenizer_l, text_encoder=clip_encoder_l, loras=[], skipped_layers=0),
+            clip_g=CLIPField(tokenizer=tokenizer_g, text_encoder=clip_encoder_g, loras=[], skipped_layers=0),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer_t5, text_encoder=t5_encoder),
+            vae=VAEField(vae=vae),
+        )

invokeai/app/invocations/sd3_text_encoder.py

@@ -0,0 +1,199 @@
+from contextlib import ExitStack
+from typing import Iterator, Tuple
+
+import torch
+from transformers import (
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5Tokenizer,
+    T5TokenizerFast,
+)
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
+from invokeai.app.invocations.model import CLIPField, T5EncoderField
+from invokeai.app.invocations.primitives import SD3ConditioningOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoRAPatcher
+from invokeai.backend.model_manager.config import ModelFormat
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, SD3ConditioningInfo
+
+# The SD3 T5 Max Sequence Length set based on the default in diffusers.
+SD3_T5_MAX_SEQ_LEN = 256
+
+
+@invocation(
+    "sd3_text_encoder",
+    title="SD3 Text Encoding",
+    tags=["prompt", "conditioning", "sd3"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class Sd3TextEncoderInvocation(BaseInvocation):
+    """Encodes and preps a prompt for a SD3 image."""
+
+    clip_l: CLIPField = InputField(
+        title="CLIP L",
+        description=FieldDescriptions.clip,
+        input=Input.Connection,
+    )
+    clip_g: CLIPField = InputField(
+        title="CLIP G",
+        description=FieldDescriptions.clip,
+        input=Input.Connection,
+    )
+
+    # The SD3 models were trained with text encoder dropout, so the T5 encoder can be omitted to save time/memory.
+    t5_encoder: T5EncoderField | None = InputField(
+        title="T5Encoder",
+        default=None,
+        description=FieldDescriptions.t5_encoder,
+        input=Input.Connection,
+    )
+    prompt: str = InputField(description="Text prompt to encode.")
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> SD3ConditioningOutput:
+        # Note: The text encoding model are run in separate functions to ensure that all model references are locally
+        # scoped. This ensures that earlier models can be freed and gc'd before loading later models (if necessary).
+
+        clip_l_embeddings, clip_l_pooled_embeddings = self._clip_encode(context, self.clip_l)
+        clip_g_embeddings, clip_g_pooled_embeddings = self._clip_encode(context, self.clip_g)
+
+        t5_embeddings: torch.Tensor | None = None
+        if self.t5_encoder is not None:
+            t5_embeddings = self._t5_encode(context, SD3_T5_MAX_SEQ_LEN)
+
+        conditioning_data = ConditioningFieldData(
+            conditionings=[
+                SD3ConditioningInfo(
+                    clip_l_embeds=clip_l_embeddings,
+                    clip_l_pooled_embeds=clip_l_pooled_embeddings,
+                    clip_g_embeds=clip_g_embeddings,
+                    clip_g_pooled_embeds=clip_g_pooled_embeddings,
+                    t5_embeds=t5_embeddings,
+                )
+            ]
+        )
+
+        conditioning_name = context.conditioning.save(conditioning_data)
+        return SD3ConditioningOutput.build(conditioning_name)
+
+    def _t5_encode(self, context: InvocationContext, max_seq_len: int) -> torch.Tensor:
+        assert self.t5_encoder is not None
+        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
+        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
+
+        prompt = [self.prompt]
+
+        with (
+            t5_text_encoder_info as t5_text_encoder,
+            t5_tokenizer_info as t5_tokenizer,
+        ):
+            assert isinstance(t5_text_encoder, T5EncoderModel)
+            assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast))
+
+            text_inputs = t5_tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=max_seq_len,
+                truncation=True,
+                add_special_tokens=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = t5_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+            assert isinstance(text_input_ids, torch.Tensor)
+            assert isinstance(untruncated_ids, torch.Tensor)
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = t5_tokenizer.batch_decode(untruncated_ids[:, max_seq_len - 1 : -1])
+                context.logger.warning(
+                    "The following part of your input was truncated because `max_sequence_length` is set to "
+                    f" {max_seq_len} tokens: {removed_text}"
+                )
+
+            prompt_embeds = t5_text_encoder(text_input_ids.to(t5_text_encoder.device))[0]
+
+        assert isinstance(prompt_embeds, torch.Tensor)
+        return prompt_embeds
+
+    def _clip_encode(
+        self, context: InvocationContext, clip_model: CLIPField, tokenizer_max_length: int = 77
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        clip_tokenizer_info = context.models.load(clip_model.tokenizer)
+        clip_text_encoder_info = context.models.load(clip_model.text_encoder)
+
+        prompt = [self.prompt]
+
+        with (
+            clip_text_encoder_info.model_on_device() as (cached_weights, clip_text_encoder),
+            clip_tokenizer_info as clip_tokenizer,
+            ExitStack() as exit_stack,
+        ):
+            assert isinstance(clip_text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
+            assert isinstance(clip_tokenizer, CLIPTokenizer)
+
+            clip_text_encoder_config = clip_text_encoder_info.config
+            assert clip_text_encoder_config is not None
+
+            # Apply LoRA models to the CLIP encoder.
+            # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
+            if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
+                # The model is non-quantized, so we can apply the LoRA weights directly into the model.
+                exit_stack.enter_context(
+                    LoRAPatcher.apply_lora_patches(
+                        model=clip_text_encoder,
+                        patches=self._clip_lora_iterator(context, clip_model),
+                        prefix=FLUX_LORA_CLIP_PREFIX,
+                        cached_weights=cached_weights,
+                    )
+                )
+            else:
+                # There are currently no supported CLIP quantized models. Add support here if needed.
+                raise ValueError(f"Unsupported model format: {clip_text_encoder_config.format}")
+
+            clip_text_encoder = clip_text_encoder.eval().requires_grad_(False)
+
+            text_inputs = clip_tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=tokenizer_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = clip_tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+            assert isinstance(text_input_ids, torch.Tensor)
+            assert isinstance(untruncated_ids, torch.Tensor)
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = clip_tokenizer.batch_decode(untruncated_ids[:, tokenizer_max_length - 1 : -1])
+                context.logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {tokenizer_max_length} tokens: {removed_text}"
+                )
+            prompt_embeds = clip_text_encoder(
+                input_ids=text_input_ids.to(clip_text_encoder.device), output_hidden_states=True
+            )
+            pooled_prompt_embeds = prompt_embeds[0]
+            prompt_embeds = prompt_embeds.hidden_states[-2]
+
+            return prompt_embeds, pooled_prompt_embeds
+
+    def _clip_lora_iterator(
+        self, context: InvocationContext, clip_model: CLIPField
+    ) -> Iterator[Tuple[LoRAModelRaw, float]]:
+        for lora in clip_model.loras:
+            lora_info = context.models.load(lora.lora)
+            assert isinstance(lora_info.model, LoRAModelRaw)
+            yield (lora_info.model, lora.weight)
+            del lora_info

invokeai/app/services/shared/invocation_context.py

@@ -1,4 +1,3 @@
-from copy import deepcopy
 from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING, Callable, Optional, Union
@@ -222,7 +221,7 @@ class ImagesInterface(InvocationContextInterface):
         )
 
     def get_pil(self, image_name: str, mode: IMAGE_MODES | None = None) -> Image:
-        """Gets an image as a PIL Image object. This method returns a copy of the image.
+        """Gets an image as a PIL Image object.
 
         Args:
             image_name: The name of the image to get.
@@ -234,15 +233,11 @@ class ImagesInterface(InvocationContextInterface):
         image = self._services.images.get_pil_image(image_name)
         if mode and mode != image.mode:
             try:
-                # convert makes a copy!
                 image = image.convert(mode)
             except ValueError:
                 self._services.logger.warning(
                     f"Could not convert image from {image.mode} to {mode}. Using original mode instead."
                 )
-        else:
-            # copy the image to prevent the user from modifying the original
-            image = image.copy()
         return image
 
     def get_metadata(self, image_name: str) -> Optional[MetadataField]:
@@ -295,15 +290,15 @@ class TensorsInterface(InvocationContextInterface):
         return name
 
     def load(self, name: str) -> Tensor:
-        """Loads a tensor by name. This method returns a copy of the tensor.
+        """Loads a tensor by name.
 
         Args:
             name: The name of the tensor to load.
 
         Returns:
-            The tensor.
+            The loaded tensor.
         """
-        return self._services.tensors.load(name).clone()
+        return self._services.tensors.load(name)
 
 
 class ConditioningInterface(InvocationContextInterface):
@@ -321,16 +316,16 @@ class ConditioningInterface(InvocationContextInterface):
         return name
 
     def load(self, name: str) -> ConditioningFieldData:
-        """Loads conditioning data by name. This method returns a copy of the conditioning data.
+        """Loads conditioning data by name.
 
         Args:
             name: The name of the conditioning data to load.
 
         Returns:
-            The conditioning data.
+            The loaded conditioning data.
         """
 
-        return deepcopy(self._services.conditioning.load(name))
+        return self._services.conditioning.load(name)
 
 
 class ModelsInterface(InvocationContextInterface):

invokeai/app/util/step_callback.py

@@ -34,6 +34,25 @@ SD1_5_LATENT_RGB_FACTORS = [
     [-0.1307, -0.1874, -0.7445],  # L4
 ]
 
+SD3_5_LATENT_RGB_FACTORS = [
+    [-0.05240681, 0.03251581, 0.0749016],
+    [-0.0580572, 0.00759826, 0.05729818],
+    [0.16144888, 0.01270368, -0.03768577],
+    [0.14418615, 0.08460266, 0.15941818],
+    [0.04894035, 0.0056485, -0.06686988],
+    [0.05187166, 0.19222395, 0.06261094],
+    [0.1539433, 0.04818359, 0.07103094],
+    [-0.08601796, 0.09013458, 0.10893912],
+    [-0.12398469, -0.06766567, 0.0033688],
+    [-0.0439737, 0.07825329, 0.02258823],
+    [0.03101129, 0.06382551, 0.07753657],
+    [-0.01315361, 0.08554491, -0.08772475],
+    [0.06464487, 0.05914605, 0.13262741],
+    [-0.07863674, -0.02261737, -0.12761454],
+    [-0.09923835, -0.08010759, -0.06264447],
+    [-0.03392309, -0.0804029, -0.06078822],
+]
+
 FLUX_LATENT_RGB_FACTORS = [
     [-0.0412, 0.0149, 0.0521],
     [0.0056, 0.0291, 0.0768],
@@ -110,6 +129,9 @@ def stable_diffusion_step_callback(
         sdxl_latent_rgb_factors = torch.tensor(SDXL_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
         sdxl_smooth_matrix = torch.tensor(SDXL_SMOOTH_MATRIX, dtype=sample.dtype, device=sample.device)
         image = sample_to_lowres_estimated_image(sample, sdxl_latent_rgb_factors, sdxl_smooth_matrix)
+    elif base_model == BaseModelType.StableDiffusion3:
+        sd3_latent_rgb_factors = torch.tensor(SD3_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
+        image = sample_to_lowres_estimated_image(sample, sd3_latent_rgb_factors)
     else:
         v1_5_latent_rgb_factors = torch.tensor(SD1_5_LATENT_RGB_FACTORS, dtype=sample.dtype, device=sample.device)
         image = sample_to_lowres_estimated_image(sample, v1_5_latent_rgb_factors)

invokeai/backend/model_manager/config.py

@@ -53,6 +53,7 @@ class BaseModelType(str, Enum):
     Any = "any"
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
+    StableDiffusion3 = "sd-3"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
     Flux = "flux"
@@ -83,8 +84,10 @@ class SubModelType(str, Enum):
     Transformer = "transformer"
     TextEncoder = "text_encoder"
     TextEncoder2 = "text_encoder_2"
+    TextEncoder3 = "text_encoder_3"
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
+    Tokenizer3 = "tokenizer_3"
     VAE = "vae"
     VAEDecoder = "vae_decoder"
     VAEEncoder = "vae_encoder"
@@ -147,6 +150,11 @@ class ModelSourceType(str, Enum):
 DEFAULTS_PRECISION = Literal["fp16", "fp32"]
 
 
+class SubmodelDefinition(BaseModel):
+    path_or_prefix: str
+    model_type: ModelType
+
+
 class MainModelDefaultSettings(BaseModel):
     vae: str | None = Field(default=None, description="Default VAE for this model (model key)")
     vae_precision: DEFAULTS_PRECISION | None = Field(default=None, description="Default VAE precision for this model")
@@ -193,6 +201,9 @@ class ModelConfigBase(BaseModel):
         schema["required"].extend(["key", "type", "format"])
 
     model_config = ConfigDict(validate_assignment=True, json_schema_extra=json_schema_extra)
+    submodels: Optional[Dict[SubModelType, SubmodelDefinition]] = Field(
+        description="Loadable submodels in this model", default=None
+    )
 
 
 class CheckpointConfigBase(ModelConfigBase):

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

@@ -128,9 +128,9 @@ class BnbQuantizedLlmInt8bCheckpointModel(ModelLoader):
                 "The bnb modules are not available. Please install bitsandbytes if available on your platform."
             )
         match submodel_type:
-            case SubModelType.Tokenizer2:
+            case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
                 return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
+            case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 te2_model_path = Path(config.path) / "text_encoder_2"
                 model_config = AutoConfig.from_pretrained(te2_model_path)
                 with accelerate.init_empty_weights():
@@ -172,9 +172,9 @@ class T5EncoderCheckpointModel(ModelLoader):
             raise ValueError("Only T5EncoderConfig models are currently supported here.")
 
         match submodel_type:
-            case SubModelType.Tokenizer2:
+            case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
                 return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
+            case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2", torch_dtype="auto")
 
         raise ValueError(

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

@@ -42,6 +42,7 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 @ModelLoaderRegistry.register(
     base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Diffusers
 )
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion3, type=ModelType.Main, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Checkpoint)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Checkpoint)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Checkpoint)
@@ -51,13 +52,6 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 class StableDiffusionDiffusersModel(GenericDiffusersLoader):
     """Class to load main models."""
 
-    model_base_to_model_type = {
-        BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",
-        BaseModelType.StableDiffusion2: "FrozenOpenCLIPEmbedder",
-        BaseModelType.StableDiffusionXL: "SDXL",
-        BaseModelType.StableDiffusionXLRefiner: "SDXL-Refiner",
-    }
-
     def _load_model(
         self,
         config: AnyModelConfig,
@@ -117,6 +111,8 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
             load_class = load_classes[config.base][config.variant]
         except KeyError as e:
             raise Exception(f"No diffusers pipeline known for base={config.base}, variant={config.variant}") from e
+        prediction_type = config.prediction_type.value
+        upcast_attention = config.upcast_attention
 
         # Without SilenceWarnings we get log messages like this:
         # site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
@@ -127,7 +123,13 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
         # ['text_model.embeddings.position_ids']
 
         with SilenceWarnings():
-            pipeline = load_class.from_single_file(config.path, torch_dtype=self._torch_dtype)
+            pipeline = load_class.from_single_file(
+                config.path,
+                torch_dtype=self._torch_dtype,
+                prediction_type=prediction_type,
+                upcast_attention=upcast_attention,
+                load_safety_checker=False,
+            )
 
         if not submodel_type:
             return pipeline

invokeai/backend/model_manager/probe.py

@@ -19,7 +19,7 @@ from invokeai.backend.lora.conversions.flux_diffusers_lora_conversion_utils impo
     is_state_dict_likely_in_flux_diffusers_format,
 )
 from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import is_state_dict_likely_in_flux_kohya_format
-from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
+from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
@@ -33,7 +33,10 @@ from invokeai.backend.model_manager.config import (
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
+    SubmodelDefinition,
+    SubModelType,
 )
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import ConfigLoader
 from invokeai.backend.model_manager.util.model_util import lora_token_vector_length, read_checkpoint_meta
 from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
@@ -112,6 +115,7 @@ class ModelProbe(object):
         "StableDiffusionXLPipeline": ModelType.Main,
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
+        "StableDiffusion3Pipeline": ModelType.Main,
         "LatentConsistencyModelPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.VAE,
         "AutoencoderTiny": ModelType.VAE,
@@ -122,6 +126,8 @@ class ModelProbe(object):
         "CLIPTextModel": ModelType.CLIPEmbed,
         "T5EncoderModel": ModelType.T5Encoder,
         "FluxControlNetModel": ModelType.ControlNet,
+        "SD3Transformer2DModel": ModelType.Main,
+        "CLIPTextModelWithProjection": ModelType.CLIPEmbed,
     }
 
     @classmethod
@@ -178,7 +184,7 @@ class ModelProbe(object):
             fields.get("description") or f"{fields['base'].value} {model_type.value} model {fields['name']}"
         )
         fields["format"] = ModelFormat(fields.get("format")) if "format" in fields else probe.get_format()
-        fields["hash"] = fields.get("hash") or ModelHash(algorithm=hash_algo).hash(model_path)
+        fields["hash"] = "placeholder"  # fields.get("hash") or ModelHash(algorithm=hash_algo).hash(model_path)
 
         fields["default_settings"] = fields.get("default_settings")
 
@@ -217,6 +223,10 @@ class ModelProbe(object):
                 and fields["prediction_type"] == SchedulerPredictionType.VPrediction
             )
 
+        get_submodels = getattr(probe, "get_submodels", None)
+        if fields["base"] == BaseModelType.StableDiffusion3 and callable(get_submodels):
+            fields["submodels"] = get_submodels()
+
         model_info = ModelConfigFactory.make_config(fields)  # , key=fields.get("key", None))
         return model_info
 
@@ -462,9 +472,8 @@ MODEL_NAME_TO_PREPROCESSOR = {
     "normal": "normalbae_image_processor",
     "sketch": "pidi_image_processor",
     "scribble": "lineart_image_processor",
-    "lineart anime": "lineart_anime_image_processor",
-    "lineart_anime": "lineart_anime_image_processor",
     "lineart": "lineart_image_processor",
+    "lineart_anime": "lineart_anime_image_processor",
     "softedge": "hed_image_processor",
     "hed": "hed_image_processor",
     "shuffle": "content_shuffle_image_processor",
@@ -747,18 +756,33 @@ class FolderProbeBase(ProbeBase):
 
 class PipelineFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
-        with open(self.model_path / "unet" / "config.json", "r") as file:
-            unet_conf = json.load(file)
-        if unet_conf["cross_attention_dim"] == 768:
-            return BaseModelType.StableDiffusion1
-        elif unet_conf["cross_attention_dim"] == 1024:
-            return BaseModelType.StableDiffusion2
-        elif unet_conf["cross_attention_dim"] == 1280:
-            return BaseModelType.StableDiffusionXLRefiner
-        elif unet_conf["cross_attention_dim"] == 2048:
-            return BaseModelType.StableDiffusionXL
-        else:
-            raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+        # Handle pipelines with a UNet (i.e SD 1.x, SD2, SDXL).
+        config_path = self.model_path / "unet" / "config.json"
+        if config_path.exists():
+            with open(config_path) as file:
+                unet_conf = json.load(file)
+            if unet_conf["cross_attention_dim"] == 768:
+                return BaseModelType.StableDiffusion1
+            elif unet_conf["cross_attention_dim"] == 1024:
+                return BaseModelType.StableDiffusion2
+            elif unet_conf["cross_attention_dim"] == 1280:
+                return BaseModelType.StableDiffusionXLRefiner
+            elif unet_conf["cross_attention_dim"] == 2048:
+                return BaseModelType.StableDiffusionXL
+            else:
+                raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+
+        # Handle pipelines with a transformer (i.e. SD3).
+        config_path = self.model_path / "transformer" / "config.json"
+        if config_path.exists():
+            with open(config_path) as file:
+                transformer_conf = json.load(file)
+            if transformer_conf["_class_name"] == "SD3Transformer2DModel":
+                return BaseModelType.StableDiffusion3
+            else:
+                raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+
+        raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
 
     def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
         with open(self.model_path / "scheduler" / "scheduler_config.json", "r") as file:
@@ -770,6 +794,21 @@ class PipelineFolderProbe(FolderProbeBase):
         else:
             raise InvalidModelConfigException("Unknown scheduler prediction type: {scheduler_conf['prediction_type']}")
 
+    def get_submodels(self) -> Dict[SubModelType, SubmodelDefinition]:
+        config = ConfigLoader.load_config(self.model_path, config_name="model_index.json")
+        submodels: Dict[SubModelType, SubmodelDefinition] = {}
+        for key, value in config.items():
+            if key.startswith("_") or not (isinstance(value, list) and len(value) == 2):
+                continue
+            model_loader = str(value[1])
+            if model_type := ModelProbe.CLASS2TYPE.get(model_loader):
+                submodels[SubModelType(key)] = SubmodelDefinition(
+                    path_or_prefix=(self.model_path / key).resolve().as_posix(),
+                    model_type=model_type,
+                )
+
+        return submodels
+
     def get_variant_type(self) -> ModelVariantType:
         # This only works for pipelines! Any kind of
         # exception results in our returning the

invokeai/backend/model_manager/starter_models.py

@@ -13,9 +13,6 @@ class StarterModelWithoutDependencies(BaseModel):
     type: ModelType
     format: Optional[ModelFormat] = None
     is_installed: bool = False
-    # allows us to track what models a user has installed across name changes within starter models
-    # if you update a starter model name, please add the old one to this list for that starter model
-    previous_names: list[str] = []
 
 
 class StarterModel(StarterModelWithoutDependencies):
@@ -246,49 +243,44 @@ easy_neg_sd1 = StarterModel(
 # endregion
 # region IP Adapter
 ip_adapter_sd1 = StarterModel(
-    name="Standard Reference (IP Adapter)",
+    name="IP Adapter",
     base=BaseModelType.StableDiffusion1,
     source="https://huggingface.co/InvokeAI/ip_adapter_sd15/resolve/main/ip-adapter_sd15.safetensors",
-    description="References images with a more generalized/looser degree of precision.",
+    description="IP-Adapter for SD 1.5 models",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sd_image_encoder],
-    previous_names=["IP Adapter"],
 )
 ip_adapter_plus_sd1 = StarterModel(
-    name="Precise Reference (IP Adapter Plus)",
+    name="IP Adapter Plus",
     base=BaseModelType.StableDiffusion1,
     source="https://huggingface.co/InvokeAI/ip_adapter_plus_sd15/resolve/main/ip-adapter-plus_sd15.safetensors",
-    description="References images with a higher degree of precision.",
+    description="Refined IP-Adapter for SD 1.5 models",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sd_image_encoder],
-    previous_names=["IP Adapter Plus"],
 )
 ip_adapter_plus_face_sd1 = StarterModel(
-    name="Face Reference (IP Adapter Plus Face)",
+    name="IP Adapter Plus Face",
     base=BaseModelType.StableDiffusion1,
     source="https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15/resolve/main/ip-adapter-plus-face_sd15.safetensors",
-    description="References images with a higher degree of precision, adapted for faces",
+    description="Refined IP-Adapter for SD 1.5 models, adapted for faces",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sd_image_encoder],
-    previous_names=["IP Adapter Plus Face"],
 )
 ip_adapter_sdxl = StarterModel(
-    name="Standard Reference (IP Adapter ViT-H)",
+    name="IP Adapter SDXL",
     base=BaseModelType.StableDiffusionXL,
     source="https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h/resolve/main/ip-adapter_sdxl_vit-h.safetensors",
-    description="References images with a higher degree of precision.",
+    description="IP-Adapter for SDXL models",
     type=ModelType.IPAdapter,
     dependencies=[ip_adapter_sdxl_image_encoder],
-    previous_names=["IP Adapter SDXL"],
 )
 ip_adapter_flux = StarterModel(
-    name="Standard Reference (XLabs FLUX IP-Adapter)",
+    name="XLabs FLUX IP-Adapter",
     base=BaseModelType.Flux,
     source="https://huggingface.co/XLabs-AI/flux-ip-adapter/resolve/main/flux-ip-adapter.safetensors",
-    description="References images with a more generalized/looser degree of precision.",
+    description="FLUX IP-Adapter",
     type=ModelType.IPAdapter,
     dependencies=[clip_vit_l_image_encoder],
-    previous_names=["XLabs FLUX IP-Adapter"],
 )
 # endregion
 # region ControlNet
@@ -307,162 +299,157 @@ qr_code_cnet_sdxl = StarterModel(
     type=ModelType.ControlNet,
 )
 canny_sd1 = StarterModel(
-    name="Hard Edge Detection (canny)",
+    name="canny",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_canny",
-    description="Uses detected edges in the image to control composition.",
+    description="ControlNet weights trained on sd-1.5 with canny conditioning.",
     type=ModelType.ControlNet,
-    previous_names=["canny"],
 )
 inpaint_cnet_sd1 = StarterModel(
-    name="Inpainting",
+    name="inpaint",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_inpaint",
     description="ControlNet weights trained on sd-1.5 with canny conditioning, inpaint version",
     type=ModelType.ControlNet,
-    previous_names=["inpaint"],
 )
 mlsd_sd1 = StarterModel(
-    name="Line Drawing (mlsd)",
+    name="mlsd",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_mlsd",
-    description="Uses straight line detection for controlling the generation.",
+    description="ControlNet weights trained on sd-1.5 with canny conditioning, MLSD version",
     type=ModelType.ControlNet,
-    previous_names=["mlsd"],
 )
 depth_sd1 = StarterModel(
-    name="Depth Map",
+    name="depth",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11f1p_sd15_depth",
-    description="Uses depth information in the image to control the depth in the generation.",
+    description="ControlNet weights trained on sd-1.5 with depth conditioning",
     type=ModelType.ControlNet,
-    previous_names=["depth"],
 )
 normal_bae_sd1 = StarterModel(
-    name="Lighting Detection (Normals)",
+    name="normal_bae",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_normalbae",
-    description="Uses detected lighting information to guide the lighting of the composition.",
+    description="ControlNet weights trained on sd-1.5 with normalbae image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["normal_bae"],
 )
 seg_sd1 = StarterModel(
-    name="Segmentation Map",
+    name="seg",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_seg",
-    description="Uses segmentation maps to guide the structure of the composition.",
+    description="ControlNet weights trained on sd-1.5 with seg image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["seg"],
 )
 lineart_sd1 = StarterModel(
-    name="Lineart",
+    name="lineart",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_lineart",
-    description="Uses lineart detection to guide the lighting of the composition.",
+    description="ControlNet weights trained on sd-1.5 with lineart image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["lineart"],
 )
 lineart_anime_sd1 = StarterModel(
-    name="Lineart Anime",
+    name="lineart_anime",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15s2_lineart_anime",
-    description="Uses anime lineart detection to guide the lighting of the composition.",
+    description="ControlNet weights trained on sd-1.5 with anime image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["lineart_anime"],
 )
 openpose_sd1 = StarterModel(
-    name="Pose Detection (openpose)",
+    name="openpose",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_openpose",
-    description="Uses pose information to control the pose of human characters in the generation.",
+    description="ControlNet weights trained on sd-1.5 with openpose image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["openpose"],
 )
 scribble_sd1 = StarterModel(
-    name="Contour Detection (scribble)",
+    name="scribble",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_scribble",
-    description="Uses edges, contours, or line art in the image to control composition.",
+    description="ControlNet weights trained on sd-1.5 with scribble image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["scribble"],
 )
 softedge_sd1 = StarterModel(
-    name="Soft Edge Detection (softedge)",
+    name="softedge",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11p_sd15_softedge",
-    description="Uses a soft edge detection map to control composition.",
+    description="ControlNet weights trained on sd-1.5 with soft edge conditioning",
     type=ModelType.ControlNet,
-    previous_names=["softedge"],
 )
 shuffle_sd1 = StarterModel(
-    name="Remix (shuffle)",
+    name="shuffle",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11e_sd15_shuffle",
     description="ControlNet weights trained on sd-1.5 with shuffle image conditioning",
     type=ModelType.ControlNet,
-    previous_names=["shuffle"],
 )
 tile_sd1 = StarterModel(
-    name="Tile",
+    name="tile",
     base=BaseModelType.StableDiffusion1,
     source="lllyasviel/control_v11f1e_sd15_tile",
-    description="Uses image data to replicate exact colors/structure in the resulting generation.",
+    description="ControlNet weights trained on sd-1.5 with tiled image conditioning",
+    type=ModelType.ControlNet,
+)
+ip2p_sd1 = StarterModel(
+    name="ip2p",
+    base=BaseModelType.StableDiffusion1,
+    source="lllyasviel/control_v11e_sd15_ip2p",
+    description="ControlNet weights trained on sd-1.5 with ip2p conditioning.",
     type=ModelType.ControlNet,
-    previous_names=["tile"],
 )
 canny_sdxl = StarterModel(
-    name="Hard Edge Detection (canny)",
+    name="canny-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-canny-sdxl-1.0",
-    description="Uses detected edges in the image to control composition.",
+    description="ControlNet weights trained on sdxl-1.0 with canny conditioning, by Xinsir.",
     type=ModelType.ControlNet,
-    previous_names=["canny-sdxl"],
 )
 depth_sdxl = StarterModel(
-    name="Depth Map",
+    name="depth-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="diffusers/controlNet-depth-sdxl-1.0",
-    description="Uses depth information in the image to control the depth in the generation.",
+    description="ControlNet weights trained on sdxl-1.0 with depth conditioning.",
     type=ModelType.ControlNet,
-    previous_names=["depth-sdxl"],
 )
 softedge_sdxl = StarterModel(
-    name="Soft Edge Detection (softedge)",
+    name="softedge-dexined-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="SargeZT/controlNet-sd-xl-1.0-softedge-dexined",
-    description="Uses a soft edge detection map to control composition.",
+    description="ControlNet weights trained on sdxl-1.0 with dexined soft edge preprocessing.",
+    type=ModelType.ControlNet,
+)
+depth_zoe_16_sdxl = StarterModel(
+    name="depth-16bit-zoe-sdxl",
+    base=BaseModelType.StableDiffusionXL,
+    source="SargeZT/controlNet-sd-xl-1.0-depth-16bit-zoe",
+    description="ControlNet weights trained on sdxl-1.0 with Zoe's preprocessor (16 bits).",
+    type=ModelType.ControlNet,
+)
+depth_zoe_32_sdxl = StarterModel(
+    name="depth-zoe-sdxl",
+    base=BaseModelType.StableDiffusionXL,
+    source="diffusers/controlNet-zoe-depth-sdxl-1.0",
+    description="ControlNet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).",
     type=ModelType.ControlNet,
-    previous_names=["softedge-dexined-sdxl"],
 )
 openpose_sdxl = StarterModel(
-    name="Pose Detection (openpose)",
+    name="openpose-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-openpose-sdxl-1.0",
-    description="Uses pose information to control the pose of human characters in the generation.",
+    description="ControlNet weights trained on sdxl-1.0 compatible with the DWPose processor by Xinsir.",
     type=ModelType.ControlNet,
-    previous_names=["openpose-sdxl", "controlnet-openpose-sdxl"],
 )
 scribble_sdxl = StarterModel(
-    name="Contour Detection (scribble)",
+    name="scribble-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-scribble-sdxl-1.0",
-    description="Uses edges, contours, or line art in the image to control composition.",
+    description="ControlNet weights trained on sdxl-1.0 compatible with various lineart processors and black/white sketches by Xinsir.",
     type=ModelType.ControlNet,
-    previous_names=["scribble-sdxl", "controlnet-scribble-sdxl"],
 )
 tile_sdxl = StarterModel(
-    name="Tile",
+    name="tile-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="xinsir/controlNet-tile-sdxl-1.0",
-    description="Uses image data to replicate exact colors/structure in the resulting generation.",
-    type=ModelType.ControlNet,
-    previous_names=["tile-sdxl"],
-)
-union_cnet_sdxl = StarterModel(
-    name="Multi-Guidance Detection (Union Pro)",
-    base=BaseModelType.StableDiffusionXL,
-    source="InvokeAI/Xinsir-SDXL_Controlnet_Union",
-    description="A unified ControlNet for SDXL model that supports 10+ control types",
+    description="ControlNet weights trained on sdxl-1.0 with tiled image conditioning",
     type=ModelType.ControlNet,
 )
 union_cnet_flux = StarterModel(
@@ -475,52 +462,60 @@ union_cnet_flux = StarterModel(
 # endregion
 # region T2I Adapter
 t2i_canny_sd1 = StarterModel(
-    name="Hard Edge Detection (canny)",
+    name="canny-sd15",
     base=BaseModelType.StableDiffusion1,
     source="TencentARC/t2iadapter_canny_sd15v2",
-    description="Uses detected edges in the image to control composition",
+    description="T2I Adapter weights trained on sd-1.5 with canny conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["canny-sd15"],
 )
 t2i_sketch_sd1 = StarterModel(
-    name="Sketch",
+    name="sketch-sd15",
     base=BaseModelType.StableDiffusion1,
     source="TencentARC/t2iadapter_sketch_sd15v2",
-    description="Uses a sketch to control composition",
+    description="T2I Adapter weights trained on sd-1.5 with sketch conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["sketch-sd15"],
 )
 t2i_depth_sd1 = StarterModel(
-    name="Depth Map",
+    name="depth-sd15",
     base=BaseModelType.StableDiffusion1,
     source="TencentARC/t2iadapter_depth_sd15v2",
-    description="Uses depth information in the image to control the depth in the generation.",
+    description="T2I Adapter weights trained on sd-1.5 with depth conditioning.",
+    type=ModelType.T2IAdapter,
+)
+t2i_zoe_depth_sd1 = StarterModel(
+    name="zoedepth-sd15",
+    base=BaseModelType.StableDiffusion1,
+    source="TencentARC/t2iadapter_zoedepth_sd15v1",
+    description="T2I Adapter weights trained on sd-1.5 with zoe depth conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["depth-sd15"],
 )
 t2i_canny_sdxl = StarterModel(
-    name="Hard Edge Detection (canny)",
+    name="canny-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="TencentARC/t2i-adapter-canny-sdxl-1.0",
-    description="Uses detected edges in the image to control composition",
+    description="T2I Adapter weights trained on sdxl-1.0 with canny conditioning.",
+    type=ModelType.T2IAdapter,
+)
+t2i_zoe_depth_sdxl = StarterModel(
+    name="zoedepth-sdxl",
+    base=BaseModelType.StableDiffusionXL,
+    source="TencentARC/t2i-adapter-depth-zoe-sdxl-1.0",
+    description="T2I Adapter weights trained on sdxl-1.0 with zoe depth conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["canny-sdxl"],
 )
 t2i_lineart_sdxl = StarterModel(
-    name="Lineart",
+    name="lineart-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="TencentARC/t2i-adapter-lineart-sdxl-1.0",
-    description="Uses lineart detection to guide the lighting of the composition.",
+    description="T2I Adapter weights trained on sdxl-1.0 with lineart conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["lineart-sdxl"],
 )
 t2i_sketch_sdxl = StarterModel(
-    name="Sketch",
+    name="sketch-sdxl",
     base=BaseModelType.StableDiffusionXL,
     source="TencentARC/t2i-adapter-sketch-sdxl-1.0",
-    description="Uses a sketch to control composition",
+    description="T2I Adapter weights trained on sdxl-1.0 with sketch conditioning.",
     type=ModelType.T2IAdapter,
-    previous_names=["sketch-sdxl"],
 )
 # endregion
 # region SpandrelImageToImage
@@ -605,18 +600,22 @@ STARTER_MODELS: list[StarterModel] = [
     softedge_sd1,
     shuffle_sd1,
     tile_sd1,
+    ip2p_sd1,
     canny_sdxl,
     depth_sdxl,
     softedge_sdxl,
+    depth_zoe_16_sdxl,
+    depth_zoe_32_sdxl,
     openpose_sdxl,
     scribble_sdxl,
     tile_sdxl,
-    union_cnet_sdxl,
     union_cnet_flux,
     t2i_canny_sd1,
     t2i_sketch_sd1,
     t2i_depth_sd1,
+    t2i_zoe_depth_sd1,
     t2i_canny_sdxl,
+    t2i_zoe_depth_sdxl,
     t2i_lineart_sdxl,
     t2i_sketch_sdxl,
     realesrgan_x4,
@@ -647,6 +646,7 @@ sd1_bundle: list[StarterModel] = [
     softedge_sd1,
     shuffle_sd1,
     tile_sd1,
+    ip2p_sd1,
     swinir,
 ]
 
@@ -657,6 +657,8 @@ sdxl_bundle: list[StarterModel] = [
     canny_sdxl,
     depth_sdxl,
     softedge_sdxl,
+    depth_zoe_16_sdxl,
+    depth_zoe_32_sdxl,
     openpose_sdxl,
     scribble_sdxl,
     tile_sdxl,

invokeai/backend/model_manager/util/select_hf_files.py

@@ -129,8 +129,10 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
 
             # Some special handling is needed here if there is not an exact match and if we cannot infer the variant
             # from the file name. In this case, we only give this file a point if the requested variant is FP32 or DEFAULT.
-            if candidate_variant_label == f".{variant}" or (
-                not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.Default]
+            if (
+                candidate_variant_label
+                and candidate_variant_label.startswith(f".{variant.value}")
+                or (not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.Default])
             ):
                 score += 1
 
@@ -146,7 +148,7 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
         # Check if at least one of the files has the explicit fp16 variant.
         at_least_one_fp16 = False
         for candidate in candidate_list:
-            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0] == ".fp16":
+            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0].startswith(".fp16"):
                 at_least_one_fp16 = True
                 break
 
@@ -162,7 +164,16 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
         # candidate.
         highest_score_candidate = max(candidate_list, key=lambda candidate: candidate.score)
         if highest_score_candidate:
-            result.add(highest_score_candidate.path)
+            pattern = r"^(.*?)-\d+-of-\d+(\.\w+)$"
+            match = re.match(pattern, highest_score_candidate.path.as_posix())
+            if match:
+                for candidate in candidate_list:
+                    if candidate.path.as_posix().startswith(match.group(1)) and candidate.path.as_posix().endswith(
+                        match.group(2)
+                    ):
+                        result.add(candidate.path)
+            else:
+                result.add(highest_score_candidate.path)
 
     # If one of the architecture-related variants was specified and no files matched other than
     # config and text files then we return an empty list

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -49,9 +49,32 @@ class FLUXConditioningInfo:
         return self
 
 
+@dataclass
+class SD3ConditioningInfo:
+    clip_l_pooled_embeds: torch.Tensor
+    clip_l_embeds: torch.Tensor
+    clip_g_pooled_embeds: torch.Tensor
+    clip_g_embeds: torch.Tensor
+    t5_embeds: torch.Tensor | None
+
+    def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
+        self.clip_l_pooled_embeds = self.clip_l_pooled_embeds.to(device=device, dtype=dtype)
+        self.clip_l_embeds = self.clip_l_embeds.to(device=device, dtype=dtype)
+        self.clip_g_pooled_embeds = self.clip_g_pooled_embeds.to(device=device, dtype=dtype)
+        self.clip_g_embeds = self.clip_g_embeds.to(device=device, dtype=dtype)
+        if self.t5_embeds is not None:
+            self.t5_embeds = self.t5_embeds.to(device=device, dtype=dtype)
+        return self
+
+
 @dataclass
 class ConditioningFieldData:
-    conditionings: List[BasicConditioningInfo] | List[SDXLConditioningInfo] | List[FLUXConditioningInfo]
+    conditionings: (
+        List[BasicConditioningInfo]
+        | List[SDXLConditioningInfo]
+        | List[FLUXConditioningInfo]
+        | List[SD3ConditioningInfo]
+    )
 
 
 @dataclass

invokeai/backend/stable_diffusion/extensions/preview.py

@@ -33,7 +33,7 @@ class PreviewExt(ExtensionBase):
     def initial_preview(self, ctx: DenoiseContext):
         self.callback(
             PipelineIntermediateState(
-                step=0,
+                step=-1,
                 order=ctx.scheduler.order,
                 total_steps=len(ctx.inputs.timesteps),
                 timestep=int(ctx.scheduler.config.num_train_timesteps),  # TODO: is there any code which uses it?

invokeai/frontend/web/package.json

@@ -58,7 +58,7 @@
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.1.0",
-    "@invoke-ai/ui-library": "^0.0.43",
+    "@invoke-ai/ui-library": "^0.0.42",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",

invokeai/frontend/web/pnpm-lock.yaml

@@ -24,8 +24,8 @@ dependencies:
     specifier: ^5.1.0
     version: 5.1.0
   '@invoke-ai/ui-library':
-    specifier: ^0.0.43
-    version: 0.0.43(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^0.0.42
+    version: 0.0.42(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1)
   '@nanostores/react':
     specifier: ^0.7.3
     version: 0.7.3(nanostores@0.11.3)(react@18.3.1)
@@ -1696,20 +1696,20 @@ packages:
       prettier: 3.3.3
     dev: true
 
-  /@invoke-ai/ui-library@0.0.43(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-t3fPYyks07ue3dEBPJuTHbeDLnDckDCOrtvc07mMDbLOnlPEZ0StaeiNGH+oO8qLzAuMAlSTdswgHfzTc2MmPw==}
+  /@invoke-ai/ui-library@0.0.42(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.1.0)(@types/react@18.3.11)(i18next@23.15.1)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-OuDXRipBO5mu+Nv4qN8cd8MiwiGBdq6h4PirVgPI9/ltbdcIzePgUJ0dJns26lflHSTRWW38I16wl4YTw3mNWA==}
     peerDependencies:
       '@fontsource-variable/inter': ^5.0.16
       react: ^18.2.0
       react-dom: ^18.2.0
     dependencies:
-      '@chakra-ui/anatomy': 2.3.4
+      '@chakra-ui/anatomy': 2.2.2
       '@chakra-ui/icons': 2.2.4(@chakra-ui/react@2.10.2)(react@18.3.1)
       '@chakra-ui/layout': 2.3.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/react': 2.10.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.11)(framer-motion@11.10.0)(react-dom@18.3.1)(react@18.3.1)
-      '@chakra-ui/styled-system': 2.11.2(react@18.3.1)
-      '@chakra-ui/theme-tools': 2.2.6(@chakra-ui/styled-system@2.11.2)(react@18.3.1)
+      '@chakra-ui/styled-system': 2.9.2
+      '@chakra-ui/theme-tools': 2.1.2(@chakra-ui/styled-system@2.9.2)
       '@emotion/react': 11.13.3(@types/react@18.3.11)(react@18.3.1)
       '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.11)(react@18.3.1)
       '@fontsource-variable/inter': 5.1.0

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

@@ -94,7 +94,6 @@
         "close": "Close",
         "copy": "Copy",
         "copyError": "$t(gallery.copy) Error",
-        "clipboard": "Clipboard",
         "on": "On",
         "off": "Off",
         "or": "or",
@@ -682,8 +681,7 @@
         "recallParameters": "Recall Parameters",
         "recallParameter": "Recall {{label}}",
         "scheduler": "Scheduler",
-        "seamlessXAxis": "Seamless X Axis",
-        "seamlessYAxis": "Seamless Y Axis",
+        "seamless": "Seamless",
         "seed": "Seed",
         "steps": "Steps",
         "strength": "Image to image strength",
@@ -714,12 +712,8 @@
         "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
         "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
-        "noDefaultSettings": "No default settings configured for this model. Visit the Model Manager to add default settings.",
         "defaultSettings": "Default Settings",
         "defaultSettingsSaved": "Default Settings Saved",
-        "defaultSettingsOutOfSync": "Some settings do not match the model's defaults:",
-        "restoreDefaultSettings": "Click to use the model's default settings.",
-        "usingDefaultSettings": "Using model's default settings",
         "delete": "Delete",
         "deleteConfig": "Delete Config",
         "deleteModel": "Delete Model",
@@ -804,6 +798,7 @@
         "uploadImage": "Upload Image",
         "urlOrLocalPath": "URL or Local Path",
         "urlOrLocalPathHelper": "URLs should point to a single file. Local paths can point to a single file or folder for a single diffusers model.",
+        "useDefaultSettings": "Use Default Settings",
         "vae": "VAE",
         "vaePrecision": "VAE Precision",
         "variant": "Variant",
@@ -1113,9 +1108,6 @@
         "enableInformationalPopovers": "Enable Informational Popovers",
         "informationalPopoversDisabled": "Informational Popovers Disabled",
         "informationalPopoversDisabledDesc": "Informational popovers have been disabled. Enable them in Settings.",
-        "enableModelDescriptions": "Enable Model Descriptions in Dropdowns",
-        "modelDescriptionsDisabled": "Model Descriptions in Dropdowns Disabled",
-        "modelDescriptionsDisabledDesc": "Model descriptions in dropdowns have been disabled. Enable them in Settings.",
         "enableInvisibleWatermark": "Enable Invisible Watermark",
         "enableNSFWChecker": "Enable NSFW Checker",
         "general": "General",
@@ -1259,33 +1251,6 @@
             "heading": "Mask Adjustments",
             "paragraphs": ["Adjust the mask."]
         },
-        "inpainting": {
-            "heading": "Inpainting",
-            "paragraphs": ["Controls which area is modified, guided by Denoising Strength."]
-        },
-        "rasterLayer": {
-            "heading": "Raster Layer",
-            "paragraphs": ["Pixel-based content of your canvas, used during image generation."]
-        },
-        "regionalGuidance": {
-            "heading": "Regional Guidance",
-            "paragraphs": ["Brush to guide where elements from global prompts should appear."]
-        },
-        "regionalGuidanceAndReferenceImage": {
-            "heading": "Regional Guidance and Regional Reference Image",
-            "paragraphs": [
-                "For Regional Guidance, brush to guide where elements from global prompts should appear.",
-                "For Regional Reference Image, brush to apply a reference image to specific areas."
-            ]
-        },
-        "globalReferenceImage": {
-            "heading": "Global Reference Image",
-            "paragraphs": ["Applies a reference image to influence the entire generation."]
-        },
-        "regionalReferenceImage": {
-            "heading": "Regional Reference Image",
-            "paragraphs": ["Brush to apply a reference image to specific areas."]
-        },
         "controlNet": {
             "heading": "ControlNet",
             "paragraphs": [
@@ -1683,8 +1648,6 @@
         "controlLayer": "Control Layer",
         "inpaintMask": "Inpaint Mask",
         "regionalGuidance": "Regional Guidance",
-        "canvasAsRasterLayer": "$t(controlLayers.canvas) as $t(controlLayers.rasterLayer)",
-        "canvasAsControlLayer": "$t(controlLayers.canvas) as $t(controlLayers.controlLayer)",
         "referenceImage": "Reference Image",
         "regionalReferenceImage": "Regional Reference Image",
         "globalReferenceImage": "Global Reference Image",
@@ -1725,18 +1688,8 @@
         "layer_other": "Layers",
         "layer_withCount_one": "Layer ({{count}})",
         "layer_withCount_other": "Layers ({{count}})",
-        "convertRasterLayerTo": "Convert $t(controlLayers.rasterLayer) To",
-        "convertControlLayerTo": "Convert $t(controlLayers.controlLayer) To",
-        "convertInpaintMaskTo": "Convert $t(controlLayers.inpaintMask) To",
-        "convertRegionalGuidanceTo": "Convert $t(controlLayers.regionalGuidance) To",
-        "copyRasterLayerTo": "Copy $t(controlLayers.rasterLayer) To",
-        "copyControlLayerTo": "Copy $t(controlLayers.controlLayer) To",
-        "copyInpaintMaskTo": "Copy $t(controlLayers.inpaintMask) To",
-        "copyRegionalGuidanceTo": "Copy $t(controlLayers.regionalGuidance) To",
-        "newRasterLayer": "New $t(controlLayers.rasterLayer)",
-        "newControlLayer": "New $t(controlLayers.controlLayer)",
-        "newInpaintMask": "New $t(controlLayers.inpaintMask)",
-        "newRegionalGuidance": "New $t(controlLayers.regionalGuidance)",
+        "convertToControlLayer": "Convert to Control Layer",
+        "convertToRasterLayer": "Convert to Raster Layer",
         "transparency": "Transparency",
         "enableTransparencyEffect": "Enable Transparency Effect",
         "disableTransparencyEffect": "Disable Transparency Effect",
@@ -1760,7 +1713,6 @@
         "newGallerySessionDesc": "This will clear the canvas and all settings except for your model selection. Generations will be sent to the gallery.",
         "newCanvasSession": "New Canvas Session",
         "newCanvasSessionDesc": "This will clear the canvas and all settings except for your model selection. Generations will be staged on the canvas.",
-        "replaceCurrent": "Replace Current",
         "controlMode": {
             "controlMode": "Control Mode",
             "balanced": "Balanced",
@@ -1890,24 +1842,16 @@
             "apply": "Apply",
             "cancel": "Cancel"
         },
-        "selectObject": {
-            "selectObject": "Select Object",
+        "segment": {
+            "autoMask": "Auto Mask",
             "pointType": "Point Type",
-            "invertSelection": "Invert Selection",
-            "include": "Include",
-            "exclude": "Exclude",
+            "foreground": "Foreground",
+            "background": "Background",
             "neutral": "Neutral",
-            "apply": "Apply",
             "reset": "Reset",
-            "saveAs": "Save As",
+            "apply": "Apply",
             "cancel": "Cancel",
-            "process": "Process",
-            "help1": "Select a single target object. Add <Bold>Include</Bold> and <Bold>Exclude</Bold> points to indicate which parts of the layer are part of the target object.",
-            "help2": "Start with one <Bold>Include</Bold> point within the target object. Add more points to refine the selection. Fewer points typically produce better results.",
-            "help3": "Invert the selection to select everything except the target object.",
-            "clickToAdd": "Click on the layer to add a point",
-            "dragToMove": "Drag a point to move it",
-            "clickToRemove": "Click on a point to remove it"
+            "process": "Process"
         },
         "settings": {
             "snapToGrid": {
@@ -1948,8 +1892,6 @@
             "newRegionalReferenceImage": "New Regional Reference Image",
             "newControlLayer": "New Control Layer",
             "newRasterLayer": "New Raster Layer",
-            "newInpaintMask": "New Inpaint Mask",
-            "newRegionalGuidance": "New Regional Guidance",
             "cropCanvasToBbox": "Crop Canvas to Bbox"
         },
         "stagingArea": {
@@ -2082,11 +2024,13 @@
     },
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
-        "line1": "<ItalicComponent>Select Object</ItalicComponent> tool for precise object selection and editing",
-        "line2": "Expanded Flux support, now with Global Reference Images",
-        "line3": "Improved tooltips and context menus",
-        "readReleaseNotes": "Read Release Notes",
-        "watchRecentReleaseVideos": "Watch Recent Release Videos",
-        "watchUiUpdatesOverview": "Watch UI Updates Overview"
+        "canvasV2Announcement": {
+            "newCanvas": "A powerful new control canvas",
+            "newLayerTypes": "New layer types for even more control",
+            "fluxSupport": "Support for the Flux family of models",
+            "readReleaseNotes": "Read Release Notes",
+            "watchReleaseVideo": "Watch Release Video",
+            "watchUiUpdatesOverview": "Watch UI Updates Overview"
+        }
     }
 }

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

@@ -8,7 +8,6 @@ import {
   controlLayerAdded,
   entityRasterized,
   entitySelected,
-  inpaintMaskAdded,
   rasterLayerAdded,
   referenceImageAdded,
   referenceImageIPAdapterImageChanged,
@@ -18,7 +17,6 @@ import {
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import type {
   CanvasControlLayerState,
-  CanvasInpaintMaskState,
   CanvasRasterLayerState,
   CanvasReferenceImageState,
   CanvasRegionalGuidanceState,
@@ -112,46 +110,6 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
         return;
       }
 
-      /**
-
-      /**
-       * Image dropped on Inpaint Mask
-       */
-      if (
-        overData.actionType === 'ADD_INPAINT_MASK_FROM_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = selectCanvasSlice(getState()).bbox.rect;
-        const overrides: Partial<CanvasInpaintMaskState> = {
-          objects: [imageObject],
-          position: { x, y },
-        };
-        dispatch(inpaintMaskAdded({ overrides, isSelected: true }));
-        return;
-      }
-
-      /**
-
-      /**
-       * Image dropped on Regional Guidance
-       */
-      if (
-        overData.actionType === 'ADD_REGIONAL_GUIDANCE_FROM_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = selectCanvasSlice(getState()).bbox.rect;
-        const overrides: Partial<CanvasRegionalGuidanceState> = {
-          objects: [imageObject],
-          position: { x, y },
-        };
-        dispatch(rgAdded({ overrides, isSelected: true }));
-        return;
-      }
-
       /**
        * Image dropped on Raster layer
        */

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

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

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

@@ -23,10 +23,8 @@ export type Feature =
   | 'dynamicPrompts'
   | 'dynamicPromptsMaxPrompts'
   | 'dynamicPromptsSeedBehaviour'
-  | 'globalReferenceImage'
   | 'imageFit'
   | 'infillMethod'
-  | 'inpainting'
   | 'ipAdapterMethod'
   | 'lora'
   | 'loraWeight'
@@ -48,7 +46,6 @@ export type Feature =
   | 'paramVAEPrecision'
   | 'paramWidth'
   | 'patchmatchDownScaleSize'
-  | 'rasterLayer'
   | 'refinerModel'
   | 'refinerNegativeAestheticScore'
   | 'refinerPositiveAestheticScore'
@@ -56,9 +53,6 @@ export type Feature =
   | 'refinerStart'
   | 'refinerSteps'
   | 'refinerCfgScale'
-  | 'regionalGuidance'
-  | 'regionalGuidanceAndReferenceImage'
-  | 'regionalReferenceImage'
   | 'scaleBeforeProcessing'
   | 'seamlessTilingXAxis'
   | 'seamlessTilingYAxis'
@@ -82,24 +76,6 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
   clipSkip: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000178161-advanced-settings',
   },
-  inpainting: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000096702-inpainting-outpainting-and-bounding-box',
-  },
-  rasterLayer: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000094998-raster-layers-and-initial-images',
-  },
-  regionalGuidance: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000165024-regional-guidance-layers',
-  },
-  regionalGuidanceAndReferenceImage: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000165024-regional-guidance-layers',
-  },
-  globalReferenceImage: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000159340-global-and-regional-reference-images-ip-adapters-',
-  },
-  regionalReferenceImage: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000159340-global-and-regional-reference-images-ip-adapters-',
-  },
   controlNet: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000105880',
   },

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

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

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

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

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

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

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

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

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

@@ -1,6 +1,5 @@
 import { Button, Flex, Heading } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 import {
   useAddControlLayer,
   useAddGlobalReferenceImage,
@@ -29,80 +28,69 @@ export const CanvasAddEntityButtons = memo(() => {
       <Flex position="relative" flexDir="column" gap={4} top="20%">
         <Flex flexDir="column" justifyContent="flex-start" gap={2}>
           <Heading size="xs">{t('controlLayers.global')}</Heading>
-          <InformationalPopover feature="globalReferenceImage">
-            <Button
-              size="sm"
-              variant="ghost"
-              justifyContent="flex-start"
-              leftIcon={<PiPlusBold />}
-              onClick={addGlobalReferenceImage}
-            >
-              {t('controlLayers.globalReferenceImage')}
-            </Button>
-          </InformationalPopover>
+          <Button
+            size="sm"
+            variant="ghost"
+            justifyContent="flex-start"
+            leftIcon={<PiPlusBold />}
+            onClick={addGlobalReferenceImage}
+          >
+            {t('controlLayers.globalReferenceImage')}
+          </Button>
         </Flex>
         <Flex flexDir="column" gap={2}>
           <Heading size="xs">{t('controlLayers.regional')}</Heading>
-          <InformationalPopover feature="inpainting">
-            <Button
-              size="sm"
-              variant="ghost"
-              justifyContent="flex-start"
-              leftIcon={<PiPlusBold />}
-              onClick={addInpaintMask}
-            >
-              {t('controlLayers.inpaintMask')}
-            </Button>
-          </InformationalPopover>
-          <InformationalPopover feature="regionalGuidance">
-            <Button
-              size="sm"
-              variant="ghost"
-              justifyContent="flex-start"
-              leftIcon={<PiPlusBold />}
-              onClick={addRegionalGuidance}
-              isDisabled={isFLUX}
-            >
-              {t('controlLayers.regionalGuidance')}
-            </Button>
-          </InformationalPopover>
-          <InformationalPopover feature="regionalReferenceImage">
-            <Button
-              size="sm"
-              variant="ghost"
-              justifyContent="flex-start"
-              leftIcon={<PiPlusBold />}
-              onClick={addRegionalReferenceImage}
-              isDisabled={isFLUX}
-            >
-              {t('controlLayers.regionalReferenceImage')}
-            </Button>
-          </InformationalPopover>
+          <Button
+            size="sm"
+            variant="ghost"
+            justifyContent="flex-start"
+            leftIcon={<PiPlusBold />}
+            onClick={addInpaintMask}
+          >
+            {t('controlLayers.inpaintMask')}
+          </Button>
+          <Button
+            size="sm"
+            variant="ghost"
+            justifyContent="flex-start"
+            leftIcon={<PiPlusBold />}
+            onClick={addRegionalGuidance}
+            isDisabled={isFLUX}
+          >
+            {t('controlLayers.regionalGuidance')}
+          </Button>
+          <Button
+            size="sm"
+            variant="ghost"
+            justifyContent="flex-start"
+            leftIcon={<PiPlusBold />}
+            onClick={addRegionalReferenceImage}
+            isDisabled={isFLUX}
+          >
+            {t('controlLayers.regionalReferenceImage')}
+          </Button>
         </Flex>
         <Flex flexDir="column" justifyContent="flex-start" gap={2}>
           <Heading size="xs">{t('controlLayers.layer_other')}</Heading>
-          <InformationalPopover feature="controlNet">
-            <Button
-              size="sm"
-              variant="ghost"
-              justifyContent="flex-start"
-              leftIcon={<PiPlusBold />}
-              onClick={addControlLayer}
-            >
-              {t('controlLayers.controlLayer')}
-            </Button>
-          </InformationalPopover>
-          <InformationalPopover feature="rasterLayer">
-            <Button
-              size="sm"
-              variant="ghost"
-              justifyContent="flex-start"
-              leftIcon={<PiPlusBold />}
-              onClick={addRasterLayer}
-            >
-              {t('controlLayers.rasterLayer')}
-            </Button>
-          </InformationalPopover>
+
+          <Button
+            size="sm"
+            variant="ghost"
+            justifyContent="flex-start"
+            leftIcon={<PiPlusBold />}
+            onClick={addControlLayer}
+          >
+            {t('controlLayers.controlLayer')}
+          </Button>
+          <Button
+            size="sm"
+            variant="ghost"
+            justifyContent="flex-start"
+            leftIcon={<PiPlusBold />}
+            onClick={addRasterLayer}
+          >
+            {t('controlLayers.rasterLayer')}
+          </Button>
         </Flex>
       </Flex>
     </Flex>

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

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

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

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

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

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

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

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

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

@@ -1,43 +1,42 @@
 import { MenuGroup } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
-import { ControlLayerMenuItems } from 'features/controlLayers/components/ControlLayer/ControlLayerMenuItems';
-import { InpaintMaskMenuItems } from 'features/controlLayers/components/InpaintMask/InpaintMaskMenuItems';
-import { IPAdapterMenuItems } from 'features/controlLayers/components/IPAdapter/IPAdapterMenuItems';
-import { RasterLayerMenuItems } from 'features/controlLayers/components/RasterLayer/RasterLayerMenuItems';
-import { RegionalGuidanceMenuItems } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceMenuItems';
+import { CanvasEntityMenuItemsCopyToClipboard } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCopyToClipboard';
+import { CanvasEntityMenuItemsCropToBbox } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCropToBbox';
+import { CanvasEntityMenuItemsDelete } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDelete';
+import { CanvasEntityMenuItemsFilter } from 'features/controlLayers/components/common/CanvasEntityMenuItemsFilter';
+import { CanvasEntityMenuItemsSave } from 'features/controlLayers/components/common/CanvasEntityMenuItemsSave';
+import { CanvasEntityMenuItemsSegment } from 'features/controlLayers/components/common/CanvasEntityMenuItemsSegment';
+import { CanvasEntityMenuItemsTransform } from 'features/controlLayers/components/common/CanvasEntityMenuItemsTransform';
 import {
   EntityIdentifierContext,
   useEntityIdentifierContext,
 } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useEntityTypeString } from 'features/controlLayers/hooks/useEntityTypeString';
+import { useEntityTitle } from 'features/controlLayers/hooks/useEntityTitle';
 import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import type { PropsWithChildren } from 'react';
+import {
+  isFilterableEntityIdentifier,
+  isSaveableEntityIdentifier,
+  isSegmentableEntityIdentifier,
+  isTransformableEntityIdentifier,
+} from 'features/controlLayers/store/types';
 import { memo } from 'react';
-import type { Equals } from 'tsafe';
-import { assert } from 'tsafe';
 
 const CanvasContextMenuSelectedEntityMenuItemsContent = memo(() => {
   const entityIdentifier = useEntityIdentifierContext();
+  const title = useEntityTitle(entityIdentifier);
 
-  if (entityIdentifier.type === 'raster_layer') {
-    return <RasterLayerMenuItems />;
-  }
-  if (entityIdentifier.type === 'control_layer') {
-    return <ControlLayerMenuItems />;
-  }
-  if (entityIdentifier.type === 'inpaint_mask') {
-    return <InpaintMaskMenuItems />;
-  }
-  if (entityIdentifier.type === 'regional_guidance') {
-    return <RegionalGuidanceMenuItems />;
-  }
-  if (entityIdentifier.type === 'reference_image') {
-    return <IPAdapterMenuItems />;
-  }
-
-  assert<Equals<typeof entityIdentifier.type, never>>(false);
+  return (
+    <MenuGroup title={title}>
+      {isFilterableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsFilter />}
+      {isTransformableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsTransform />}
+      {isSegmentableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsSegment />}
+      {isSaveableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsCopyToClipboard />}
+      {isSaveableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsSave />}
+      {isTransformableEntityIdentifier(entityIdentifier) && <CanvasEntityMenuItemsCropToBbox />}
+      <CanvasEntityMenuItemsDelete />
+    </MenuGroup>
+  );
 });
-
 CanvasContextMenuSelectedEntityMenuItemsContent.displayName = 'CanvasContextMenuSelectedEntityMenuItemsContent';
 
 export const CanvasContextMenuSelectedEntityMenuItems = memo(() => {
@@ -49,20 +48,9 @@ export const CanvasContextMenuSelectedEntityMenuItems = memo(() => {
 
   return (
     <EntityIdentifierContext.Provider value={selectedEntityIdentifier}>
-      <CanvasContextMenuSelectedEntityMenuGroup>
-        <CanvasContextMenuSelectedEntityMenuItemsContent />
-      </CanvasContextMenuSelectedEntityMenuGroup>
+      <CanvasContextMenuSelectedEntityMenuItemsContent />
     </EntityIdentifierContext.Provider>
   );
 });
 
 CanvasContextMenuSelectedEntityMenuItems.displayName = 'CanvasContextMenuSelectedEntityMenuItems';
-
-const CanvasContextMenuSelectedEntityMenuGroup = memo((props: PropsWithChildren) => {
-  const entityIdentifier = useEntityIdentifierContext();
-  const title = useEntityTypeString(entityIdentifier.type);
-
-  return <MenuGroup title={title}>{props.children}</MenuGroup>;
-});
-
-CanvasContextMenuSelectedEntityMenuGroup.displayName = 'CanvasContextMenuSelectedEntityMenuGroup';

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

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

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

@@ -29,7 +29,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
     <Menu>
       <MenuButton
         as={IconButton}
-        minW={8}
+        size="sm"
         variant="link"
         alignSelf="stretch"
         tooltip={t('controlLayers.addLayer')}

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

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

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

@@ -23,7 +23,7 @@ export const EntityListSelectedEntityActionBarDuplicateButton = memo(() => {
     <IconButton
       onClick={onClick}
       isDisabled={!selectedEntityIdentifier || isBusy}
-      minW={8}
+      size="sm"
       variant="link"
       alignSelf="stretch"
       aria-label={t('controlLayers.duplicate')}

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

@@ -5,7 +5,7 @@ import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/sel
 import { isFilterableEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiShootingStarFill } from 'react-icons/pi';
+import { PiShootingStarBold } from 'react-icons/pi';
 
 export const EntityListSelectedEntityActionBarFilterButton = memo(() => {
   const { t } = useTranslation();
@@ -24,12 +24,12 @@ export const EntityListSelectedEntityActionBarFilterButton = memo(() => {
     <IconButton
       onClick={filter.start}
       isDisabled={filter.isDisabled}
-      minW={8}
+      size="sm"
       variant="link"
       alignSelf="stretch"
       aria-label={t('controlLayers.filter.filter')}
       tooltip={t('controlLayers.filter.filter')}
-      icon={<PiShootingStarFill />}
+      icon={<PiShootingStarBold />}
     />
   );
 });

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

@@ -31,7 +31,7 @@ export const EntityListSelectedEntityActionBarSaveToAssetsButton = memo(() => {
     <IconButton
       onClick={onClick}
       isDisabled={!selectedEntityIdentifier || isBusy}
-      minW={8}
+      size="sm"
       variant="link"
       alignSelf="stretch"
       aria-label={t('controlLayers.saveLayerToAssets')}

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

@@ -1,37 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useEntitySegmentAnything } from 'features/controlLayers/hooks/useEntitySegmentAnything';
-import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import { isSegmentableEntityIdentifier } from 'features/controlLayers/store/types';
-import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiShapesFill } from 'react-icons/pi';
-
-export const EntityListSelectedEntityActionBarSelectObjectButton = memo(() => {
-  const { t } = useTranslation();
-  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
-  const segment = useEntitySegmentAnything(selectedEntityIdentifier);
-
-  if (!selectedEntityIdentifier) {
-    return null;
-  }
-
-  if (!isSegmentableEntityIdentifier(selectedEntityIdentifier)) {
-    return null;
-  }
-
-  return (
-    <IconButton
-      onClick={segment.start}
-      isDisabled={segment.isDisabled}
-      minW={8}
-      variant="link"
-      alignSelf="stretch"
-      aria-label={t('controlLayers.selectObject.selectObject')}
-      tooltip={t('controlLayers.selectObject.selectObject')}
-      icon={<PiShapesFill />}
-    />
-  );
-});
-
-EntityListSelectedEntityActionBarSelectObjectButton.displayName = 'EntityListSelectedEntityActionBarSelectObjectButton';

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

@@ -24,7 +24,7 @@ export const EntityListSelectedEntityActionBarTransformButton = memo(() => {
     <IconButton
       onClick={transform.start}
       isDisabled={transform.isDisabled}
-      minW={8}
+      size="sm"
       variant="link"
       alignSelf="stretch"
       aria-label={t('controlLayers.transform.transform')}

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

@@ -10,7 +10,7 @@ import { CanvasDropArea } from 'features/controlLayers/components/CanvasDropArea
 import { Filter } from 'features/controlLayers/components/Filters/Filter';
 import { CanvasHUD } from 'features/controlLayers/components/HUD/CanvasHUD';
 import { InvokeCanvasComponent } from 'features/controlLayers/components/InvokeCanvasComponent';
-import { SelectObject } from 'features/controlLayers/components/SelectObject/SelectObject';
+import { SegmentAnything } from 'features/controlLayers/components/SegmentAnything/SegmentAnything';
 import { StagingAreaIsStagingGate } from 'features/controlLayers/components/StagingArea/StagingAreaIsStagingGate';
 import { StagingAreaToolbar } from 'features/controlLayers/components/StagingArea/StagingAreaToolbar';
 import { CanvasToolbar } from 'features/controlLayers/components/Toolbar/CanvasToolbar';
@@ -25,8 +25,8 @@ const MenuContent = () => {
   return (
     <CanvasManagerProviderGate>
       <MenuList>
-        <CanvasContextMenuSelectedEntityMenuItems />
         <CanvasContextMenuGlobalMenuItems />
+        <CanvasContextMenuSelectedEntityMenuItems />
       </MenuList>
     </CanvasManagerProviderGate>
   );
@@ -71,16 +71,12 @@ export const CanvasMainPanelContent = memo(() => {
           >
             <InvokeCanvasComponent />
             <CanvasManagerProviderGate>
-              <Flex
-                position="absolute"
-                flexDir="column"
-                top={1}
-                insetInlineStart={1}
-                pointerEvents="none"
-                gap={2}
-                alignItems="flex-start"
-              >
-                {showHUD && <CanvasHUD />}
+              {showHUD && (
+                <Flex position="absolute" top={1} insetInlineStart={1} pointerEvents="none">
+                  <CanvasHUD />
+                </Flex>
+              )}
+              <Flex flexDir="column" position="absolute" top={1} insetInlineEnd={1} pointerEvents="none" gap={2}>
                 <CanvasAlertsSelectedEntityStatus />
                 <CanvasAlertsPreserveMask />
                 <CanvasAlertsSendingToGallery />
@@ -106,7 +102,7 @@ export const CanvasMainPanelContent = memo(() => {
         <CanvasManagerProviderGate>
           <Filter />
           <Transform />
-          <SelectObject />
+          <SegmentAnything />
         </CanvasManagerProviderGate>
       </Flex>
       <CanvasDropArea />

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

@@ -21,7 +21,7 @@ import { selectCanvasSlice, selectEntityOrThrow } from 'features/controlLayers/s
 import type { CanvasEntityIdentifier, ControlModeV2 } from 'features/controlLayers/store/types';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiBoundingBoxBold, PiShootingStarFill, PiUploadBold } from 'react-icons/pi';
+import { PiBoundingBoxBold, PiShootingStarBold, PiUploadBold } from 'react-icons/pi';
 import type { ControlNetModelConfig, PostUploadAction, T2IAdapterModelConfig } from 'services/api/types';
 
 const useControlLayerControlAdapter = (entityIdentifier: CanvasEntityIdentifier<'control_layer'>) => {
@@ -93,7 +93,7 @@ export const ControlLayerControlAdapter = memo(() => {
           variant="link"
           aria-label={t('controlLayers.filter.filter')}
           tooltip={t('controlLayers.filter.filter')}
-          icon={<PiShootingStarFill />}
+          icon={<PiShootingStarBold />}
         />
         <IconButton
           onClick={pullBboxIntoLayer}

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

@@ -1,15 +1,15 @@
 import { MenuDivider } from '@invoke-ai/ui-library';
 import { IconMenuItemGroup } from 'common/components/IconMenuItem';
 import { CanvasEntityMenuItemsArrange } from 'features/controlLayers/components/common/CanvasEntityMenuItemsArrange';
+import { CanvasEntityMenuItemsCopyToClipboard } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCopyToClipboard';
 import { CanvasEntityMenuItemsCropToBbox } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCropToBbox';
 import { CanvasEntityMenuItemsDelete } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDelete';
 import { CanvasEntityMenuItemsDuplicate } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDuplicate';
 import { CanvasEntityMenuItemsFilter } from 'features/controlLayers/components/common/CanvasEntityMenuItemsFilter';
 import { CanvasEntityMenuItemsSave } from 'features/controlLayers/components/common/CanvasEntityMenuItemsSave';
-import { CanvasEntityMenuItemsSelectObject } from 'features/controlLayers/components/common/CanvasEntityMenuItemsSelectObject';
+import { CanvasEntityMenuItemsSegment } from 'features/controlLayers/components/common/CanvasEntityMenuItemsSegment';
 import { CanvasEntityMenuItemsTransform } from 'features/controlLayers/components/common/CanvasEntityMenuItemsTransform';
-import { ControlLayerMenuItemsConvertToSubMenu } from 'features/controlLayers/components/ControlLayer/ControlLayerMenuItemsConvertToSubMenu';
-import { ControlLayerMenuItemsCopyToSubMenu } from 'features/controlLayers/components/ControlLayer/ControlLayerMenuItemsCopyToSubMenu';
+import { ControlLayerMenuItemsConvertControlToRaster } from 'features/controlLayers/components/ControlLayer/ControlLayerMenuItemsConvertControlToRaster';
 import { ControlLayerMenuItemsTransparencyEffect } from 'features/controlLayers/components/ControlLayer/ControlLayerMenuItemsTransparencyEffect';
 import { memo } from 'react';
 
@@ -24,12 +24,12 @@ export const ControlLayerMenuItems = memo(() => {
       <MenuDivider />
       <CanvasEntityMenuItemsTransform />
       <CanvasEntityMenuItemsFilter />
-      <CanvasEntityMenuItemsSelectObject />
+      <CanvasEntityMenuItemsSegment />
+      <ControlLayerMenuItemsConvertControlToRaster />
       <ControlLayerMenuItemsTransparencyEffect />
       <MenuDivider />
-      <ControlLayerMenuItemsCopyToSubMenu />
-      <ControlLayerMenuItemsConvertToSubMenu />
       <CanvasEntityMenuItemsCropToBbox />
+      <CanvasEntityMenuItemsCopyToClipboard />
       <CanvasEntityMenuItemsSave />
     </>
   );

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

@@ -0,0 +1,27 @@
+import { MenuItem } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
+import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
+import { controlLayerConvertedToRasterLayer } from 'features/controlLayers/store/canvasSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiLightningBold } from 'react-icons/pi';
+
+export const ControlLayerMenuItemsConvertControlToRaster = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const entityIdentifier = useEntityIdentifierContext('control_layer');
+  const isInteractable = useIsEntityInteractable(entityIdentifier);
+
+  const convertControlLayerToRasterLayer = useCallback(() => {
+    dispatch(controlLayerConvertedToRasterLayer({ entityIdentifier }));
+  }, [dispatch, entityIdentifier]);
+
+  return (
+    <MenuItem onClick={convertControlLayerToRasterLayer} icon={<PiLightningBold />} isDisabled={!isInteractable}>
+      {t('controlLayers.convertToRasterLayer')}
+    </MenuItem>
+  );
+});
+
+ControlLayerMenuItemsConvertControlToRaster.displayName = 'ControlLayerMenuItemsConvertControlToRaster';

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

@@ -1,56 +0,0 @@
-import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
-import {
-  controlLayerConvertedToInpaintMask,
-  controlLayerConvertedToRasterLayer,
-  controlLayerConvertedToRegionalGuidance,
-} from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiSwapBold } from 'react-icons/pi';
-
-export const ControlLayerMenuItemsConvertToSubMenu = memo(() => {
-  const { t } = useTranslation();
-  const subMenu = useSubMenu();
-  const dispatch = useAppDispatch();
-  const entityIdentifier = useEntityIdentifierContext('control_layer');
-  const isInteractable = useIsEntityInteractable(entityIdentifier);
-
-  const convertToInpaintMask = useCallback(() => {
-    dispatch(controlLayerConvertedToInpaintMask({ entityIdentifier, replace: true }));
-  }, [dispatch, entityIdentifier]);
-
-  const convertToRegionalGuidance = useCallback(() => {
-    dispatch(controlLayerConvertedToRegionalGuidance({ entityIdentifier, replace: true }));
-  }, [dispatch, entityIdentifier]);
-
-  const convertToRasterLayer = useCallback(() => {
-    dispatch(controlLayerConvertedToRasterLayer({ entityIdentifier, replace: true }));
-  }, [dispatch, entityIdentifier]);
-
-  return (
-    <MenuItem {...subMenu.parentMenuItemProps} icon={<PiSwapBold />}>
-      <Menu {...subMenu.menuProps}>
-        <MenuButton {...subMenu.menuButtonProps}>
-          <SubMenuButtonContent label={t('controlLayers.convertControlLayerTo')} />
-        </MenuButton>
-        <MenuList {...subMenu.menuListProps}>
-          <MenuItem onClick={convertToInpaintMask} icon={<PiSwapBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.inpaintMask')}
-          </MenuItem>
-          <MenuItem onClick={convertToRegionalGuidance} icon={<PiSwapBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.regionalGuidance')}
-          </MenuItem>
-          <MenuItem onClick={convertToRasterLayer} icon={<PiSwapBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.rasterLayer')}
-          </MenuItem>
-        </MenuList>
-      </Menu>
-    </MenuItem>
-  );
-});
-
-ControlLayerMenuItemsConvertToSubMenu.displayName = 'ControlLayerMenuItemsConvertToSubMenu';

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

@@ -1,58 +0,0 @@
-import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
-import { CanvasEntityMenuItemsCopyToClipboard } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCopyToClipboard';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
-import {
-  controlLayerConvertedToInpaintMask,
-  controlLayerConvertedToRasterLayer,
-  controlLayerConvertedToRegionalGuidance,
-} from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiCopyBold } from 'react-icons/pi';
-
-export const ControlLayerMenuItemsCopyToSubMenu = memo(() => {
-  const { t } = useTranslation();
-  const subMenu = useSubMenu();
-  const dispatch = useAppDispatch();
-  const entityIdentifier = useEntityIdentifierContext('control_layer');
-  const isInteractable = useIsEntityInteractable(entityIdentifier);
-
-  const copyToInpaintMask = useCallback(() => {
-    dispatch(controlLayerConvertedToInpaintMask({ entityIdentifier }));
-  }, [dispatch, entityIdentifier]);
-
-  const copyToRegionalGuidance = useCallback(() => {
-    dispatch(controlLayerConvertedToRegionalGuidance({ entityIdentifier }));
-  }, [dispatch, entityIdentifier]);
-
-  const copyToRasterLayer = useCallback(() => {
-    dispatch(controlLayerConvertedToRasterLayer({ entityIdentifier }));
-  }, [dispatch, entityIdentifier]);
-
-  return (
-    <MenuItem {...subMenu.parentMenuItemProps} icon={<PiCopyBold />}>
-      <Menu {...subMenu.menuProps}>
-        <MenuButton {...subMenu.menuButtonProps}>
-          <SubMenuButtonContent label={t('controlLayers.copyControlLayerTo')} />
-        </MenuButton>
-        <MenuList {...subMenu.menuListProps}>
-          <CanvasEntityMenuItemsCopyToClipboard />
-          <MenuItem onClick={copyToInpaintMask} icon={<PiCopyBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.newInpaintMask')}
-          </MenuItem>
-          <MenuItem onClick={copyToRegionalGuidance} icon={<PiCopyBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.newRegionalGuidance')}
-          </MenuItem>
-          <MenuItem onClick={copyToRasterLayer} icon={<PiCopyBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.newRasterLayer')}
-          </MenuItem>
-        </MenuList>
-      </Menu>
-    </MenuItem>
-  );
-});
-
-ControlLayerMenuItemsCopyToSubMenu.displayName = 'ControlLayerMenuItemsCopyToSubMenu';

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

@@ -1,15 +1,4 @@
-import {
-  Button,
-  ButtonGroup,
-  Flex,
-  Heading,
-  Menu,
-  MenuButton,
-  MenuItem,
-  MenuList,
-  Spacer,
-  Spinner,
-} from '@invoke-ai/ui-library';
+import { Button, ButtonGroup, Flex, Heading, Spacer } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useAppSelector } from 'app/store/storeHooks';
 import { useFocusRegion, useIsRegionFocused } from 'common/hooks/focus';
@@ -26,7 +15,7 @@ import { IMAGE_FILTERS } from 'features/controlLayers/store/filters';
 import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
 import { memo, useCallback, useMemo, useRef } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiCaretDownBold } from 'react-icons/pi';
+import { PiArrowsCounterClockwiseBold, PiCheckBold, PiShootingStarBold, PiXBold } from 'react-icons/pi';
 
 const FilterContent = memo(
   ({ adapter }: { adapter: CanvasEntityAdapterRasterLayer | CanvasEntityAdapterControlLayer }) => {
@@ -36,7 +25,7 @@ const FilterContent = memo(
     const config = useStore(adapter.filterer.$filterConfig);
     const isCanvasFocused = useIsRegionFocused('canvas');
     const isProcessing = useStore(adapter.filterer.$isProcessing);
-    const hasImageState = useStore(adapter.filterer.$hasImageState);
+    const hasProcessed = useStore(adapter.filterer.$hasProcessed);
     const autoProcess = useAppSelector(selectAutoProcess);
 
     const onChangeFilterConfig = useCallback(
@@ -57,22 +46,6 @@ const FilterContent = memo(
       return IMAGE_FILTERS[config.type].validateConfig?.(config as never) ?? true;
     }, [config]);
 
-    const saveAsInpaintMask = useCallback(() => {
-      adapter.filterer.saveAs('inpaint_mask');
-    }, [adapter.filterer]);
-
-    const saveAsRegionalGuidance = useCallback(() => {
-      adapter.filterer.saveAs('regional_guidance');
-    }, [adapter.filterer]);
-
-    const saveAsRasterLayer = useCallback(() => {
-      adapter.filterer.saveAs('raster_layer');
-    }, [adapter.filterer]);
-
-    const saveAsControlLayer = useCallback(() => {
-      adapter.filterer.saveAs('control_layer');
-    }, [adapter.filterer]);
-
     useRegisteredHotkeys({
       id: 'applyFilter',
       category: 'canvas',
@@ -116,56 +89,40 @@ const FilterContent = memo(
         <ButtonGroup isAttached={false} size="sm" w="full">
           <Button
             variant="ghost"
+            leftIcon={<PiShootingStarBold />}
             onClick={adapter.filterer.processImmediate}
+            isLoading={isProcessing}
             loadingText={t('controlLayers.filter.process')}
-            isDisabled={isProcessing || !isValid || (autoProcess && hasImageState)}
+            isDisabled={!isValid || autoProcess}
           >
             {t('controlLayers.filter.process')}
-            {isProcessing && <Spinner ms={3} boxSize={5} color="base.600" />}
           </Button>
           <Spacer />
           <Button
+            leftIcon={<PiArrowsCounterClockwiseBold />}
             onClick={adapter.filterer.reset}
-            isDisabled={isProcessing}
+            isLoading={isProcessing}
             loadingText={t('controlLayers.filter.reset')}
             variant="ghost"
           >
             {t('controlLayers.filter.reset')}
           </Button>
           <Button
-            onClick={adapter.filterer.apply}
-            loadingText={t('controlLayers.filter.apply')}
             variant="ghost"
-            isDisabled={isProcessing || !isValid || !hasImageState}
+            leftIcon={<PiCheckBold />}
+            onClick={adapter.filterer.apply}
+            isLoading={isProcessing}
+            loadingText={t('controlLayers.filter.apply')}
+            isDisabled={!isValid || !hasProcessed}
           >
             {t('controlLayers.filter.apply')}
           </Button>
-          <Menu>
-            <MenuButton
-              as={Button}
-              loadingText={t('controlLayers.selectObject.saveAs')}
-              variant="ghost"
-              isDisabled={isProcessing || !isValid || !hasImageState}
-              rightIcon={<PiCaretDownBold />}
-            >
-              {t('controlLayers.selectObject.saveAs')}
-            </MenuButton>
-            <MenuList>
-              <MenuItem isDisabled={isProcessing || !isValid || !hasImageState} onClick={saveAsInpaintMask}>
-                {t('controlLayers.newInpaintMask')}
-              </MenuItem>
-              <MenuItem isDisabled={isProcessing || !isValid || !hasImageState} onClick={saveAsRegionalGuidance}>
-                {t('controlLayers.newRegionalGuidance')}
-              </MenuItem>
-              <MenuItem isDisabled={isProcessing || !isValid || !hasImageState} onClick={saveAsControlLayer}>
-                {t('controlLayers.newControlLayer')}
-              </MenuItem>
-              <MenuItem isDisabled={isProcessing || !isValid || !hasImageState} onClick={saveAsRasterLayer}>
-                {t('controlLayers.newRasterLayer')}
-              </MenuItem>
-            </MenuList>
-          </Menu>
-          <Button variant="ghost" onClick={adapter.filterer.cancel} loadingText={t('controlLayers.filter.cancel')}>
+          <Button
+            variant="ghost"
+            leftIcon={<PiXBold />}
+            onClick={adapter.filterer.cancel}
+            loadingText={t('controlLayers.filter.cancel')}
+          >
             {t('controlLayers.filter.cancel')}
           </Button>
         </ButtonGroup>

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

@@ -1,22 +0,0 @@
-import { MenuItem } from '@invoke-ai/ui-library';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { usePullBboxIntoGlobalReferenceImage } from 'features/controlLayers/hooks/saveCanvasHooks';
-import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
-import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiBoundingBoxBold } from 'react-icons/pi';
-
-export const IPAdapterMenuItemPullBbox = memo(() => {
-  const { t } = useTranslation();
-  const entityIdentifier = useEntityIdentifierContext('reference_image');
-  const pullBboxIntoIPAdapter = usePullBboxIntoGlobalReferenceImage(entityIdentifier);
-  const isBusy = useCanvasIsBusy();
-
-  return (
-    <MenuItem onClick={pullBboxIntoIPAdapter} icon={<PiBoundingBoxBold />} isDisabled={isBusy}>
-      {t('controlLayers.pullBboxIntoReferenceImage')}
-    </MenuItem>
-  );
-});
-
-IPAdapterMenuItemPullBbox.displayName = 'IPAdapterMenuItemPullBbox';

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

@@ -1,22 +1,16 @@
-import { MenuDivider } from '@invoke-ai/ui-library';
 import { IconMenuItemGroup } from 'common/components/IconMenuItem';
 import { CanvasEntityMenuItemsArrange } from 'features/controlLayers/components/common/CanvasEntityMenuItemsArrange';
 import { CanvasEntityMenuItemsDelete } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDelete';
 import { CanvasEntityMenuItemsDuplicate } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDuplicate';
-import { IPAdapterMenuItemPullBbox } from 'features/controlLayers/components/IPAdapter/IPAdapterMenuItemPullBbox';
 import { memo } from 'react';
 
 export const IPAdapterMenuItems = memo(() => {
   return (
-    <>
-      <IconMenuItemGroup>
-        <CanvasEntityMenuItemsArrange />
-        <CanvasEntityMenuItemsDuplicate />
-        <CanvasEntityMenuItemsDelete asIcon />
-      </IconMenuItemGroup>
-      <MenuDivider />
-      <IPAdapterMenuItemPullBbox />
-    </>
+    <IconMenuItemGroup>
+      <CanvasEntityMenuItemsArrange />
+      <CanvasEntityMenuItemsDuplicate />
+      <CanvasEntityMenuItemsDelete asIcon />
+    </IconMenuItemGroup>
   );
 });
 

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

@@ -14,7 +14,7 @@ type Props = {
 };
 
 export const InpaintMask = memo(({ id }: Props) => {
-  const entityIdentifier = useMemo<CanvasEntityIdentifier<'inpaint_mask'>>(() => ({ id, type: 'inpaint_mask' }), [id]);
+  const entityIdentifier = useMemo<CanvasEntityIdentifier>(() => ({ id, type: 'inpaint_mask' }), [id]);
 
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>

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

@@ -5,8 +5,6 @@ import { CanvasEntityMenuItemsCropToBbox } from 'features/controlLayers/componen
 import { CanvasEntityMenuItemsDelete } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDelete';
 import { CanvasEntityMenuItemsDuplicate } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDuplicate';
 import { CanvasEntityMenuItemsTransform } from 'features/controlLayers/components/common/CanvasEntityMenuItemsTransform';
-import { InpaintMaskMenuItemsConvertToSubMenu } from 'features/controlLayers/components/InpaintMask/InpaintMaskMenuItemsConvertToSubMenu';
-import { InpaintMaskMenuItemsCopyToSubMenu } from 'features/controlLayers/components/InpaintMask/InpaintMaskMenuItemsCopyToSubMenu';
 import { memo } from 'react';
 
 export const InpaintMaskMenuItems = memo(() => {
@@ -20,8 +18,6 @@ export const InpaintMaskMenuItems = memo(() => {
       <MenuDivider />
       <CanvasEntityMenuItemsTransform />
       <MenuDivider />
-      <InpaintMaskMenuItemsCopyToSubMenu />
-      <InpaintMaskMenuItemsConvertToSubMenu />
       <CanvasEntityMenuItemsCropToBbox />
     </>
   );

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

@@ -1,38 +0,0 @@
-import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
-import { inpaintMaskConvertedToRegionalGuidance } from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiSwapBold } from 'react-icons/pi';
-
-export const InpaintMaskMenuItemsConvertToSubMenu = memo(() => {
-  const { t } = useTranslation();
-  const subMenu = useSubMenu();
-  const dispatch = useAppDispatch();
-  const entityIdentifier = useEntityIdentifierContext('inpaint_mask');
-  const isInteractable = useIsEntityInteractable(entityIdentifier);
-
-  const convertToRegionalGuidance = useCallback(() => {
-    dispatch(inpaintMaskConvertedToRegionalGuidance({ entityIdentifier, replace: true }));
-  }, [dispatch, entityIdentifier]);
-
-  return (
-    <MenuItem {...subMenu.parentMenuItemProps} icon={<PiSwapBold />}>
-      <Menu {...subMenu.menuProps}>
-        <MenuButton {...subMenu.menuButtonProps}>
-          <SubMenuButtonContent label={t('controlLayers.convertInpaintMaskTo')} />
-        </MenuButton>
-        <MenuList {...subMenu.menuListProps}>
-          <MenuItem onClick={convertToRegionalGuidance} icon={<PiSwapBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.regionalGuidance')}
-          </MenuItem>
-        </MenuList>
-      </Menu>
-    </MenuItem>
-  );
-});
-
-InpaintMaskMenuItemsConvertToSubMenu.displayName = 'InpaintMaskMenuItemsConvertToSubMenu';

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

@@ -1,40 +0,0 @@
-import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
-import { CanvasEntityMenuItemsCopyToClipboard } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCopyToClipboard';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
-import { inpaintMaskConvertedToRegionalGuidance } from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiCopyBold } from 'react-icons/pi';
-
-export const InpaintMaskMenuItemsCopyToSubMenu = memo(() => {
-  const { t } = useTranslation();
-  const subMenu = useSubMenu();
-  const dispatch = useAppDispatch();
-  const entityIdentifier = useEntityIdentifierContext('inpaint_mask');
-  const isInteractable = useIsEntityInteractable(entityIdentifier);
-
-  const copyToRegionalGuidance = useCallback(() => {
-    dispatch(inpaintMaskConvertedToRegionalGuidance({ entityIdentifier }));
-  }, [dispatch, entityIdentifier]);
-
-  return (
-    <MenuItem {...subMenu.parentMenuItemProps} icon={<PiCopyBold />}>
-      <Menu {...subMenu.menuProps}>
-        <MenuButton {...subMenu.menuButtonProps}>
-          <SubMenuButtonContent label={t('controlLayers.copyInpaintMaskTo')} />
-        </MenuButton>
-        <MenuList {...subMenu.menuListProps}>
-          <CanvasEntityMenuItemsCopyToClipboard />
-          <MenuItem onClick={copyToRegionalGuidance} icon={<PiCopyBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.newRegionalGuidance')}
-          </MenuItem>
-        </MenuList>
-      </Menu>
-    </MenuItem>
-  );
-});
-
-InpaintMaskMenuItemsCopyToSubMenu.displayName = 'InpaintMaskMenuItemsCopyToSubMenu';

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

@@ -1,15 +1,15 @@
 import { MenuDivider } from '@invoke-ai/ui-library';
 import { IconMenuItemGroup } from 'common/components/IconMenuItem';
 import { CanvasEntityMenuItemsArrange } from 'features/controlLayers/components/common/CanvasEntityMenuItemsArrange';
+import { CanvasEntityMenuItemsCopyToClipboard } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCopyToClipboard';
 import { CanvasEntityMenuItemsCropToBbox } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCropToBbox';
 import { CanvasEntityMenuItemsDelete } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDelete';
 import { CanvasEntityMenuItemsDuplicate } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDuplicate';
 import { CanvasEntityMenuItemsFilter } from 'features/controlLayers/components/common/CanvasEntityMenuItemsFilter';
 import { CanvasEntityMenuItemsSave } from 'features/controlLayers/components/common/CanvasEntityMenuItemsSave';
-import { CanvasEntityMenuItemsSelectObject } from 'features/controlLayers/components/common/CanvasEntityMenuItemsSelectObject';
+import { CanvasEntityMenuItemsSegment } from 'features/controlLayers/components/common/CanvasEntityMenuItemsSegment';
 import { CanvasEntityMenuItemsTransform } from 'features/controlLayers/components/common/CanvasEntityMenuItemsTransform';
-import { RasterLayerMenuItemsConvertToSubMenu } from 'features/controlLayers/components/RasterLayer/RasterLayerMenuItemsConvertToSubMenu';
-import { RasterLayerMenuItemsCopyToSubMenu } from 'features/controlLayers/components/RasterLayer/RasterLayerMenuItemsCopyToSubMenu';
+import { RasterLayerMenuItemsConvertRasterToControl } from 'features/controlLayers/components/RasterLayer/RasterLayerMenuItemsConvertRasterToControl';
 import { memo } from 'react';
 
 export const RasterLayerMenuItems = memo(() => {
@@ -23,11 +23,11 @@ export const RasterLayerMenuItems = memo(() => {
       <MenuDivider />
       <CanvasEntityMenuItemsTransform />
       <CanvasEntityMenuItemsFilter />
-      <CanvasEntityMenuItemsSelectObject />
+      <CanvasEntityMenuItemsSegment />
+      <RasterLayerMenuItemsConvertRasterToControl />
       <MenuDivider />
-      <RasterLayerMenuItemsCopyToSubMenu />
-      <RasterLayerMenuItemsConvertToSubMenu />
       <CanvasEntityMenuItemsCropToBbox />
+      <CanvasEntityMenuItemsCopyToClipboard />
       <CanvasEntityMenuItemsSave />
     </>
   );

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

@@ -0,0 +1,36 @@
+import { MenuItem } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
+import { selectDefaultControlAdapter } from 'features/controlLayers/hooks/addLayerHooks';
+import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
+import { rasterLayerConvertedToControlLayer } from 'features/controlLayers/store/canvasSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiLightningBold } from 'react-icons/pi';
+
+export const RasterLayerMenuItemsConvertRasterToControl = memo(() => {
+  const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const entityIdentifier = useEntityIdentifierContext('raster_layer');
+  const defaultControlAdapter = useAppSelector(selectDefaultControlAdapter);
+  const isInteractable = useIsEntityInteractable(entityIdentifier);
+
+  const onClick = useCallback(() => {
+    dispatch(
+      rasterLayerConvertedToControlLayer({
+        entityIdentifier,
+        overrides: {
+          controlAdapter: defaultControlAdapter,
+        },
+      })
+    );
+  }, [defaultControlAdapter, dispatch, entityIdentifier]);
+
+  return (
+    <MenuItem onClick={onClick} icon={<PiLightningBold />} isDisabled={!isInteractable}>
+      {t('controlLayers.convertToControlLayer')}
+    </MenuItem>
+  );
+});
+
+RasterLayerMenuItemsConvertRasterToControl.displayName = 'RasterLayerMenuItemsConvertRasterToControl';

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

@@ -1,65 +0,0 @@
-import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { selectDefaultControlAdapter } from 'features/controlLayers/hooks/addLayerHooks';
-import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
-import {
-  rasterLayerConvertedToControlLayer,
-  rasterLayerConvertedToInpaintMask,
-  rasterLayerConvertedToRegionalGuidance,
-} from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiSwapBold } from 'react-icons/pi';
-
-export const RasterLayerMenuItemsConvertToSubMenu = memo(() => {
-  const { t } = useTranslation();
-  const subMenu = useSubMenu();
-
-  const dispatch = useAppDispatch();
-  const entityIdentifier = useEntityIdentifierContext('raster_layer');
-  const defaultControlAdapter = useAppSelector(selectDefaultControlAdapter);
-  const isInteractable = useIsEntityInteractable(entityIdentifier);
-
-  const convertToInpaintMask = useCallback(() => {
-    dispatch(rasterLayerConvertedToInpaintMask({ entityIdentifier, replace: true }));
-  }, [dispatch, entityIdentifier]);
-
-  const convertToRegionalGuidance = useCallback(() => {
-    dispatch(rasterLayerConvertedToRegionalGuidance({ entityIdentifier, replace: true }));
-  }, [dispatch, entityIdentifier]);
-
-  const convertToControlLayer = useCallback(() => {
-    dispatch(
-      rasterLayerConvertedToControlLayer({
-        entityIdentifier,
-        replace: true,
-        overrides: { controlAdapter: defaultControlAdapter },
-      })
-    );
-  }, [defaultControlAdapter, dispatch, entityIdentifier]);
-
-  return (
-    <MenuItem {...subMenu.parentMenuItemProps} icon={<PiSwapBold />}>
-      <Menu {...subMenu.menuProps}>
-        <MenuButton {...subMenu.menuButtonProps}>
-          <SubMenuButtonContent label={t('controlLayers.convertRasterLayerTo')} />
-        </MenuButton>
-        <MenuList {...subMenu.menuListProps}>
-          <MenuItem onClick={convertToInpaintMask} icon={<PiSwapBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.inpaintMask')}
-          </MenuItem>
-          <MenuItem onClick={convertToRegionalGuidance} icon={<PiSwapBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.regionalGuidance')}
-          </MenuItem>
-          <MenuItem onClick={convertToControlLayer} icon={<PiSwapBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.controlLayer')}
-          </MenuItem>
-        </MenuList>
-      </Menu>
-    </MenuItem>
-  );
-});
-
-RasterLayerMenuItemsConvertToSubMenu.displayName = 'RasterLayerMenuItemsConvertToSubMenu';

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

@@ -1,66 +0,0 @@
-import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
-import { CanvasEntityMenuItemsCopyToClipboard } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCopyToClipboard';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { selectDefaultControlAdapter } from 'features/controlLayers/hooks/addLayerHooks';
-import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
-import {
-  rasterLayerConvertedToControlLayer,
-  rasterLayerConvertedToInpaintMask,
-  rasterLayerConvertedToRegionalGuidance,
-} from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiCopyBold } from 'react-icons/pi';
-
-export const RasterLayerMenuItemsCopyToSubMenu = memo(() => {
-  const { t } = useTranslation();
-  const subMenu = useSubMenu();
-
-  const dispatch = useAppDispatch();
-  const entityIdentifier = useEntityIdentifierContext('raster_layer');
-  const defaultControlAdapter = useAppSelector(selectDefaultControlAdapter);
-  const isInteractable = useIsEntityInteractable(entityIdentifier);
-
-  const copyToInpaintMask = useCallback(() => {
-    dispatch(rasterLayerConvertedToInpaintMask({ entityIdentifier }));
-  }, [dispatch, entityIdentifier]);
-
-  const copyToRegionalGuidance = useCallback(() => {
-    dispatch(rasterLayerConvertedToRegionalGuidance({ entityIdentifier }));
-  }, [dispatch, entityIdentifier]);
-
-  const copyToControlLayer = useCallback(() => {
-    dispatch(
-      rasterLayerConvertedToControlLayer({
-        entityIdentifier,
-        overrides: { controlAdapter: defaultControlAdapter },
-      })
-    );
-  }, [defaultControlAdapter, dispatch, entityIdentifier]);
-
-  return (
-    <MenuItem {...subMenu.parentMenuItemProps} icon={<PiCopyBold />}>
-      <Menu {...subMenu.menuProps}>
-        <MenuButton {...subMenu.menuButtonProps}>
-          <SubMenuButtonContent label={t('controlLayers.copyRasterLayerTo')} />
-        </MenuButton>
-        <MenuList {...subMenu.menuListProps}>
-          <CanvasEntityMenuItemsCopyToClipboard />
-          <MenuItem onClick={copyToInpaintMask} icon={<PiCopyBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.newInpaintMask')}
-          </MenuItem>
-          <MenuItem onClick={copyToRegionalGuidance} icon={<PiCopyBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.newRegionalGuidance')}
-          </MenuItem>
-          <MenuItem onClick={copyToControlLayer} icon={<PiCopyBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.newControlLayer')}
-          </MenuItem>
-        </MenuList>
-      </Menu>
-    </MenuItem>
-  );
-});
-
-RasterLayerMenuItemsCopyToSubMenu.displayName = 'RasterLayerMenuItemsCopyToSubMenu';

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

@@ -16,10 +16,7 @@ type Props = {
 };
 
 export const RegionalGuidance = memo(({ id }: Props) => {
-  const entityIdentifier = useMemo<CanvasEntityIdentifier<'regional_guidance'>>(
-    () => ({ id, type: 'regional_guidance' }),
-    [id]
-  );
+  const entityIdentifier = useMemo<CanvasEntityIdentifier>(() => ({ id, type: 'regional_guidance' }), [id]);
 
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>

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

@@ -1,5 +1,4 @@
-import { MenuDivider } from '@invoke-ai/ui-library';
-import { IconMenuItemGroup } from 'common/components/IconMenuItem';
+import { Flex, MenuDivider } from '@invoke-ai/ui-library';
 import { CanvasEntityMenuItemsArrange } from 'features/controlLayers/components/common/CanvasEntityMenuItemsArrange';
 import { CanvasEntityMenuItemsCropToBbox } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCropToBbox';
 import { CanvasEntityMenuItemsDelete } from 'features/controlLayers/components/common/CanvasEntityMenuItemsDelete';
@@ -7,26 +6,22 @@ import { CanvasEntityMenuItemsDuplicate } from 'features/controlLayers/component
 import { CanvasEntityMenuItemsTransform } from 'features/controlLayers/components/common/CanvasEntityMenuItemsTransform';
 import { RegionalGuidanceMenuItemsAddPromptsAndIPAdapter } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceMenuItemsAddPromptsAndIPAdapter';
 import { RegionalGuidanceMenuItemsAutoNegative } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceMenuItemsAutoNegative';
-import { RegionalGuidanceMenuItemsConvertToSubMenu } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceMenuItemsConvertToSubMenu';
-import { RegionalGuidanceMenuItemsCopyToSubMenu } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidanceMenuItemsCopyToSubMenu';
 import { memo } from 'react';
 
 export const RegionalGuidanceMenuItems = memo(() => {
   return (
     <>
-      <IconMenuItemGroup>
+      <Flex gap={2}>
         <CanvasEntityMenuItemsArrange />
         <CanvasEntityMenuItemsDuplicate />
         <CanvasEntityMenuItemsDelete asIcon />
-      </IconMenuItemGroup>
+      </Flex>
       <MenuDivider />
       <RegionalGuidanceMenuItemsAddPromptsAndIPAdapter />
       <MenuDivider />
       <CanvasEntityMenuItemsTransform />
       <RegionalGuidanceMenuItemsAutoNegative />
       <MenuDivider />
-      <RegionalGuidanceMenuItemsCopyToSubMenu />
-      <RegionalGuidanceMenuItemsConvertToSubMenu />
       <CanvasEntityMenuItemsCropToBbox />
     </>
   );

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

@@ -1,38 +0,0 @@
-import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
-import { rgConvertedToInpaintMask } from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiSwapBold } from 'react-icons/pi';
-
-export const RegionalGuidanceMenuItemsConvertToSubMenu = memo(() => {
-  const { t } = useTranslation();
-  const subMenu = useSubMenu();
-  const dispatch = useAppDispatch();
-  const entityIdentifier = useEntityIdentifierContext('regional_guidance');
-  const isInteractable = useIsEntityInteractable(entityIdentifier);
-
-  const convertToInpaintMask = useCallback(() => {
-    dispatch(rgConvertedToInpaintMask({ entityIdentifier, replace: true }));
-  }, [dispatch, entityIdentifier]);
-
-  return (
-    <MenuItem {...subMenu.parentMenuItemProps} icon={<PiSwapBold />}>
-      <Menu {...subMenu.menuProps}>
-        <MenuButton {...subMenu.menuButtonProps}>
-          <SubMenuButtonContent label={t('controlLayers.convertRegionalGuidanceTo')} />
-        </MenuButton>
-        <MenuList {...subMenu.menuListProps}>
-          <MenuItem onClick={convertToInpaintMask} icon={<PiSwapBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.inpaintMask')}
-          </MenuItem>
-        </MenuList>
-      </Menu>
-    </MenuItem>
-  );
-});
-
-RegionalGuidanceMenuItemsConvertToSubMenu.displayName = 'RegionalGuidanceMenuItemsConvertToSubMenu';

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

@@ -1,40 +0,0 @@
-import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
-import { CanvasEntityMenuItemsCopyToClipboard } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCopyToClipboard';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
-import { rgConvertedToInpaintMask } from 'features/controlLayers/store/canvasSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiCopyBold } from 'react-icons/pi';
-
-export const RegionalGuidanceMenuItemsCopyToSubMenu = memo(() => {
-  const { t } = useTranslation();
-  const subMenu = useSubMenu();
-  const dispatch = useAppDispatch();
-  const entityIdentifier = useEntityIdentifierContext('regional_guidance');
-  const isInteractable = useIsEntityInteractable(entityIdentifier);
-
-  const copyToInpaintMask = useCallback(() => {
-    dispatch(rgConvertedToInpaintMask({ entityIdentifier }));
-  }, [dispatch, entityIdentifier]);
-
-  return (
-    <MenuItem {...subMenu.parentMenuItemProps} icon={<PiCopyBold />}>
-      <Menu {...subMenu.menuProps}>
-        <MenuButton {...subMenu.menuButtonProps}>
-          <SubMenuButtonContent label={t('controlLayers.copyRegionalGuidanceTo')} />
-        </MenuButton>
-        <MenuList {...subMenu.menuListProps}>
-          <CanvasEntityMenuItemsCopyToClipboard />
-          <MenuItem onClick={copyToInpaintMask} icon={<PiCopyBold />} isDisabled={!isInteractable}>
-            {t('controlLayers.newInpaintMask')}
-          </MenuItem>
-        </MenuList>
-      </Menu>
-    </MenuItem>
-  );
-});
-
-RegionalGuidanceMenuItemsCopyToSubMenu.displayName = 'RegionalGuidanceMenuItemsCopyToSubMenu';

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

@@ -0,0 +1,124 @@
+import { Button, ButtonGroup, Flex, Heading, Spacer } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useFocusRegion, useIsRegionFocused } from 'common/hooks/focus';
+import { CanvasAutoProcessSwitch } from 'features/controlLayers/components/CanvasAutoProcessSwitch';
+import { CanvasOperationIsolatedLayerPreviewSwitch } from 'features/controlLayers/components/CanvasOperationIsolatedLayerPreviewSwitch';
+import { SegmentAnythingPointType } from 'features/controlLayers/components/SegmentAnything/SegmentAnythingPointType';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
+import type { CanvasEntityAdapterControlLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterControlLayer';
+import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterRasterLayer';
+import { selectAutoProcess } from 'features/controlLayers/store/canvasSettingsSlice';
+import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
+import { memo, useRef } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiArrowsCounterClockwiseBold, PiCheckBold, PiStarBold, PiXBold } from 'react-icons/pi';
+
+const SegmentAnythingContent = memo(
+  ({ adapter }: { adapter: CanvasEntityAdapterRasterLayer | CanvasEntityAdapterControlLayer }) => {
+    const { t } = useTranslation();
+    const ref = useRef<HTMLDivElement>(null);
+    useFocusRegion('canvas', ref, { focusOnMount: true });
+    const isCanvasFocused = useIsRegionFocused('canvas');
+    const isProcessing = useStore(adapter.segmentAnything.$isProcessing);
+    const hasPoints = useStore(adapter.segmentAnything.$hasPoints);
+    const autoProcess = useAppSelector(selectAutoProcess);
+
+    useRegisteredHotkeys({
+      id: 'applySegmentAnything',
+      category: 'canvas',
+      callback: adapter.segmentAnything.apply,
+      options: { enabled: !isProcessing && isCanvasFocused },
+      dependencies: [adapter.segmentAnything, isProcessing, isCanvasFocused],
+    });
+
+    useRegisteredHotkeys({
+      id: 'cancelSegmentAnything',
+      category: 'canvas',
+      callback: adapter.segmentAnything.cancel,
+      options: { enabled: !isProcessing && isCanvasFocused },
+      dependencies: [adapter.segmentAnything, isProcessing, isCanvasFocused],
+    });
+
+    return (
+      <Flex
+        ref={ref}
+        bg="base.800"
+        borderRadius="base"
+        p={4}
+        flexDir="column"
+        gap={4}
+        minW={420}
+        h="auto"
+        shadow="dark-lg"
+        transitionProperty="height"
+        transitionDuration="normal"
+      >
+        <Flex w="full" gap={4}>
+          <Heading size="md" color="base.300" userSelect="none">
+            {t('controlLayers.segment.autoMask')}
+          </Heading>
+          <Spacer />
+          <CanvasAutoProcessSwitch />
+          <CanvasOperationIsolatedLayerPreviewSwitch />
+        </Flex>
+
+        <SegmentAnythingPointType adapter={adapter} />
+
+        <ButtonGroup isAttached={false} size="sm" w="full">
+          <Button
+            leftIcon={<PiStarBold />}
+            onClick={adapter.segmentAnything.processImmediate}
+            isLoading={isProcessing}
+            loadingText={t('controlLayers.segment.process')}
+            variant="ghost"
+            isDisabled={!hasPoints || autoProcess}
+          >
+            {t('controlLayers.segment.process')}
+          </Button>
+          <Spacer />
+          <Button
+            leftIcon={<PiArrowsCounterClockwiseBold />}
+            onClick={adapter.segmentAnything.reset}
+            isLoading={isProcessing}
+            loadingText={t('controlLayers.segment.reset')}
+            variant="ghost"
+          >
+            {t('controlLayers.segment.reset')}
+          </Button>
+          <Button
+            leftIcon={<PiCheckBold />}
+            onClick={adapter.segmentAnything.apply}
+            isLoading={isProcessing}
+            loadingText={t('controlLayers.segment.apply')}
+            variant="ghost"
+          >
+            {t('controlLayers.segment.apply')}
+          </Button>
+          <Button
+            leftIcon={<PiXBold />}
+            onClick={adapter.segmentAnything.cancel}
+            isLoading={isProcessing}
+            loadingText={t('common.cancel')}
+            variant="ghost"
+          >
+            {t('controlLayers.segment.cancel')}
+          </Button>
+        </ButtonGroup>
+      </Flex>
+    );
+  }
+);
+
+SegmentAnythingContent.displayName = 'SegmentAnythingContent';
+
+export const SegmentAnything = () => {
+  const canvasManager = useCanvasManager();
+  const adapter = useStore(canvasManager.stateApi.$segmentingAdapter);
+
+  if (!adapter) {
+    return null;
+  }
+
+  return <SegmentAnythingContent adapter={adapter} />;
+};

invokeai/frontend/web/src/features/controlLayers/components/SegmentAnything/SegmentAnythingPointType.tsx

@@ -6,7 +6,7 @@ import { SAM_POINT_LABEL_STRING_TO_NUMBER, zSAMPointLabelString } from 'features
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
-export const SelectObjectPointType = memo(
+export const SegmentAnythingPointType = memo(
   ({ adapter }: { adapter: CanvasEntityAdapterRasterLayer | CanvasEntityAdapterControlLayer }) => {
     const { t } = useTranslation();
     const pointType = useStore(adapter.segmentAnything.$pointTypeString);
@@ -21,15 +21,18 @@ export const SelectObjectPointType = memo(
     );
 
     return (
-      <FormControl w="min-content">
-        <FormLabel m={0}>{t('controlLayers.selectObject.pointType')}</FormLabel>
+      <FormControl w="full">
+        <FormLabel>{t('controlLayers.segment.pointType')}</FormLabel>
         <RadioGroup value={pointType} onChange={onChange} w="full" size="md">
           <Flex alignItems="center" w="full" gap={4} fontWeight="semibold" color="base.300">
             <Radio value="foreground">
-              <Text>{t('controlLayers.selectObject.include')}</Text>
+              <Text>{t('controlLayers.segment.foreground')}</Text>
             </Radio>
             <Radio value="background">
-              <Text>{t('controlLayers.selectObject.exclude')}</Text>
+              <Text>{t('controlLayers.segment.background')}</Text>
+            </Radio>
+            <Radio value="neutral">
+              <Text>{t('controlLayers.segment.neutral')}</Text>
             </Radio>
           </Flex>
         </RadioGroup>
@@ -38,4 +41,4 @@ export const SelectObjectPointType = memo(
   }
 );
 
-SelectObjectPointType.displayName = 'SelectObject';
+SegmentAnythingPointType.displayName = 'SegmentAnythingPointType';

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

@@ -1,223 +0,0 @@
-import {
-  Button,
-  ButtonGroup,
-  Flex,
-  Heading,
-  Icon,
-  ListItem,
-  Menu,
-  MenuButton,
-  MenuItem,
-  MenuList,
-  Spacer,
-  Spinner,
-  Text,
-  Tooltip,
-  UnorderedList,
-} from '@invoke-ai/ui-library';
-import { useStore } from '@nanostores/react';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useFocusRegion, useIsRegionFocused } from 'common/hooks/focus';
-import { CanvasAutoProcessSwitch } from 'features/controlLayers/components/CanvasAutoProcessSwitch';
-import { CanvasOperationIsolatedLayerPreviewSwitch } from 'features/controlLayers/components/CanvasOperationIsolatedLayerPreviewSwitch';
-import { SelectObjectInvert } from 'features/controlLayers/components/SelectObject/SelectObjectInvert';
-import { SelectObjectPointType } from 'features/controlLayers/components/SelectObject/SelectObjectPointType';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import type { CanvasEntityAdapterControlLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterControlLayer';
-import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterRasterLayer';
-import { selectAutoProcess } from 'features/controlLayers/store/canvasSettingsSlice';
-import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
-import type { PropsWithChildren } from 'react';
-import { memo, useCallback, useRef } from 'react';
-import { Trans, useTranslation } from 'react-i18next';
-import { PiCaretDownBold, PiInfoBold } from 'react-icons/pi';
-
-const SelectObjectContent = memo(
-  ({ adapter }: { adapter: CanvasEntityAdapterRasterLayer | CanvasEntityAdapterControlLayer }) => {
-    const { t } = useTranslation();
-    const ref = useRef<HTMLDivElement>(null);
-    useFocusRegion('canvas', ref, { focusOnMount: true });
-    const isCanvasFocused = useIsRegionFocused('canvas');
-    const isProcessing = useStore(adapter.segmentAnything.$isProcessing);
-    const hasPoints = useStore(adapter.segmentAnything.$hasPoints);
-    const hasImageState = useStore(adapter.segmentAnything.$hasImageState);
-    const autoProcess = useAppSelector(selectAutoProcess);
-
-    const saveAsInpaintMask = useCallback(() => {
-      adapter.segmentAnything.saveAs('inpaint_mask');
-    }, [adapter.segmentAnything]);
-
-    const saveAsRegionalGuidance = useCallback(() => {
-      adapter.segmentAnything.saveAs('regional_guidance');
-    }, [adapter.segmentAnything]);
-
-    const saveAsRasterLayer = useCallback(() => {
-      adapter.segmentAnything.saveAs('raster_layer');
-    }, [adapter.segmentAnything]);
-
-    const saveAsControlLayer = useCallback(() => {
-      adapter.segmentAnything.saveAs('control_layer');
-    }, [adapter.segmentAnything]);
-
-    useRegisteredHotkeys({
-      id: 'applySegmentAnything',
-      category: 'canvas',
-      callback: adapter.segmentAnything.apply,
-      options: { enabled: !isProcessing && isCanvasFocused },
-      dependencies: [adapter.segmentAnything, isProcessing, isCanvasFocused],
-    });
-
-    useRegisteredHotkeys({
-      id: 'cancelSegmentAnything',
-      category: 'canvas',
-      callback: adapter.segmentAnything.cancel,
-      options: { enabled: !isProcessing && isCanvasFocused },
-      dependencies: [adapter.segmentAnything, isProcessing, isCanvasFocused],
-    });
-
-    return (
-      <Flex
-        ref={ref}
-        bg="base.800"
-        borderRadius="base"
-        p={4}
-        flexDir="column"
-        gap={4}
-        minW={420}
-        h="auto"
-        shadow="dark-lg"
-        transitionProperty="height"
-        transitionDuration="normal"
-      >
-        <Flex w="full" gap={4} alignItems="center">
-          <Flex gap={2}>
-            <Heading size="md" color="base.300" userSelect="none">
-              {t('controlLayers.selectObject.selectObject')}
-            </Heading>
-            <Tooltip label={<SelectObjectHelpTooltipContent />}>
-              <Flex alignItems="center">
-                <Icon as={PiInfoBold} color="base.500" />
-              </Flex>
-            </Tooltip>
-          </Flex>
-          <Spacer />
-          <CanvasAutoProcessSwitch />
-          <CanvasOperationIsolatedLayerPreviewSwitch />
-        </Flex>
-
-        <Flex w="full" justifyContent="space-between" py={2}>
-          <SelectObjectPointType adapter={adapter} />
-          <SelectObjectInvert adapter={adapter} />
-        </Flex>
-
-        <ButtonGroup isAttached={false} size="sm" w="full">
-          <Button
-            onClick={adapter.segmentAnything.processImmediate}
-            loadingText={t('controlLayers.selectObject.process')}
-            variant="ghost"
-            isDisabled={isProcessing || !hasPoints || (autoProcess && hasImageState)}
-          >
-            {t('controlLayers.selectObject.process')}
-            {isProcessing && <Spinner ms={3} boxSize={5} color="base.600" />}
-          </Button>
-          <Spacer />
-          <Button
-            onClick={adapter.segmentAnything.reset}
-            isDisabled={isProcessing || !hasPoints}
-            loadingText={t('controlLayers.selectObject.reset')}
-            variant="ghost"
-          >
-            {t('controlLayers.selectObject.reset')}
-          </Button>
-          <Button
-            onClick={adapter.segmentAnything.apply}
-            loadingText={t('controlLayers.selectObject.apply')}
-            variant="ghost"
-            isDisabled={isProcessing || !hasImageState}
-          >
-            {t('controlLayers.selectObject.apply')}
-          </Button>
-          <Menu>
-            <MenuButton
-              as={Button}
-              loadingText={t('controlLayers.selectObject.saveAs')}
-              variant="ghost"
-              isDisabled={isProcessing || !hasImageState}
-              rightIcon={<PiCaretDownBold />}
-            >
-              {t('controlLayers.selectObject.saveAs')}
-            </MenuButton>
-            <MenuList>
-              <MenuItem isDisabled={isProcessing || !hasImageState} onClick={saveAsInpaintMask}>
-                {t('controlLayers.newInpaintMask')}
-              </MenuItem>
-              <MenuItem isDisabled={isProcessing || !hasImageState} onClick={saveAsRegionalGuidance}>
-                {t('controlLayers.newRegionalGuidance')}
-              </MenuItem>
-              <MenuItem isDisabled={isProcessing || !hasImageState} onClick={saveAsControlLayer}>
-                {t('controlLayers.newControlLayer')}
-              </MenuItem>
-              <MenuItem isDisabled={isProcessing || !hasImageState} onClick={saveAsRasterLayer}>
-                {t('controlLayers.newRasterLayer')}
-              </MenuItem>
-            </MenuList>
-          </Menu>
-          <Button
-            onClick={adapter.segmentAnything.cancel}
-            isDisabled={isProcessing}
-            loadingText={t('common.cancel')}
-            variant="ghost"
-          >
-            {t('controlLayers.selectObject.cancel')}
-          </Button>
-        </ButtonGroup>
-      </Flex>
-    );
-  }
-);
-
-SelectObjectContent.displayName = 'SegmentAnythingContent';
-
-export const SelectObject = memo(() => {
-  const canvasManager = useCanvasManager();
-  const adapter = useStore(canvasManager.stateApi.$segmentingAdapter);
-
-  if (!adapter) {
-    return null;
-  }
-
-  return <SelectObjectContent adapter={adapter} />;
-});
-
-SelectObject.displayName = 'SelectObject';
-
-const Bold = (props: PropsWithChildren) => (
-  <Text as="span" fontWeight="semibold">
-    {props.children}
-  </Text>
-);
-
-const SelectObjectHelpTooltipContent = memo(() => {
-  const { t } = useTranslation();
-
-  return (
-    <Flex gap={3} flexDir="column">
-      <Text>
-        <Trans i18nKey="controlLayers.selectObject.help1" components={{ Bold: <Bold /> }} />
-      </Text>
-      <Text>
-        <Trans i18nKey="controlLayers.selectObject.help2" components={{ Bold: <Bold /> }} />
-      </Text>
-      <Text>
-        <Trans i18nKey="controlLayers.selectObject.help3" />
-      </Text>
-      <UnorderedList>
-        <ListItem>{t('controlLayers.selectObject.clickToAdd')}</ListItem>
-        <ListItem>{t('controlLayers.selectObject.dragToMove')}</ListItem>
-        <ListItem>{t('controlLayers.selectObject.clickToRemove')}</ListItem>
-      </UnorderedList>
-    </Flex>
-  );
-});
-
-SelectObjectHelpTooltipContent.displayName = 'SelectObjectHelpTooltipContent';

invokeai/frontend/web/src/features/controlLayers/components/SelectObject/SelectObjectInvert.tsx

@@ -1,26 +0,0 @@
-import { FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
-import { useStore } from '@nanostores/react';
-import type { CanvasEntityAdapterControlLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterControlLayer';
-import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterRasterLayer';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-export const SelectObjectInvert = memo(
-  ({ adapter }: { adapter: CanvasEntityAdapterRasterLayer | CanvasEntityAdapterControlLayer }) => {
-    const { t } = useTranslation();
-    const invert = useStore(adapter.segmentAnything.$invert);
-
-    const onChange = useCallback(() => {
-      adapter.segmentAnything.$invert.set(!adapter.segmentAnything.$invert.get());
-    }, [adapter.segmentAnything.$invert]);
-
-    return (
-      <FormControl w="min-content">
-        <FormLabel m={0}>{t('controlLayers.selectObject.invertSelection')}</FormLabel>
-        <Switch size="sm" isChecked={invert} onChange={onChange} />
-      </FormControl>
-    );
-  }
-);
-
-SelectObjectInvert.displayName = 'SelectObjectInvert';

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

@@ -1,4 +1,4 @@
-import { Button, ButtonGroup, Flex, Heading, Spacer, Spinner } from '@invoke-ai/ui-library';
+import { Button, ButtonGroup, Flex, Heading, Spacer } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useFocusRegion, useIsRegionFocused } from 'common/hooks/focus';
 import { CanvasOperationIsolatedLayerPreviewSwitch } from 'features/controlLayers/components/CanvasOperationIsolatedLayerPreviewSwitch';
@@ -8,6 +8,7 @@ import type { CanvasEntityAdapter } from 'features/controlLayers/konva/CanvasEnt
 import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
 import { memo, useRef } from 'react';
 import { useTranslation } from 'react-i18next';
+import { PiArrowsCounterClockwiseBold, PiCheckBold, PiXBold } from 'react-icons/pi';
 
 const TransformContent = memo(({ adapter }: { adapter: CanvasEntityAdapter }) => {
   const { t } = useTranslation();
@@ -61,28 +62,30 @@ const TransformContent = memo(({ adapter }: { adapter: CanvasEntityAdapter }) =>
 
       <TransformFitToBboxButtons adapter={adapter} />
 
-      <ButtonGroup isAttached={false} size="sm" w="full" alignItems="center">
-        {isProcessing && <Spinner ms={3} boxSize={5} color="base.600" />}
+      <ButtonGroup isAttached={false} size="sm" w="full">
         <Spacer />
         <Button
+          leftIcon={<PiArrowsCounterClockwiseBold />}
           onClick={adapter.transformer.resetTransform}
-          isDisabled={isProcessing}
+          isLoading={isProcessing}
           loadingText={t('controlLayers.transform.reset')}
           variant="ghost"
         >
           {t('controlLayers.transform.reset')}
         </Button>
         <Button
+          leftIcon={<PiCheckBold />}
           onClick={adapter.transformer.applyTransform}
-          isDisabled={isProcessing}
+          isLoading={isProcessing}
           loadingText={t('controlLayers.transform.apply')}
           variant="ghost"
         >
           {t('controlLayers.transform.apply')}
         </Button>
         <Button
+          leftIcon={<PiXBold />}
           onClick={adapter.transformer.stopTransform}
-          isDisabled={isProcessing}
+          isLoading={isProcessing}
           loadingText={t('common.cancel')}
           variant="ghost"
         >

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

@@ -4,6 +4,7 @@ import { useStore } from '@nanostores/react';
 import type { CanvasEntityAdapter } from 'features/controlLayers/konva/CanvasEntity/types';
 import { memo, useCallback, useMemo, useState } from 'react';
 import { useTranslation } from 'react-i18next';
+import { PiArrowsOutBold } from 'react-icons/pi';
 import type { Equals } from 'tsafe';
 import { assert } from 'tsafe';
 import { z } from 'zod';
@@ -59,9 +60,10 @@ export const TransformFitToBboxButtons = memo(({ adapter }: { adapter: CanvasEnt
         <Combobox options={options} value={value} onChange={onChange} isSearchable={false} isClearable={false} />
       </FormControl>
       <Button
+        leftIcon={<PiArrowsOutBold />}
         size="sm"
         onClick={onClick}
-        isDisabled={isProcessing}
+        isLoading={isProcessing}
         loadingText={t('controlLayers.transform.fitToBbox')}
         variant="ghost"
       >

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

@@ -1,11 +1,9 @@
 import type { SystemStyleObject } from '@invoke-ai/ui-library';
 import { Button, Collapse, Flex, Icon, Spacer, Text } from '@invoke-ai/ui-library';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
 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 { useEntityTypeInformationalPopover } from 'features/controlLayers/hooks/useEntityTypeInformationalPopover';
 import { useEntityTypeTitle } from 'features/controlLayers/hooks/useEntityTypeTitle';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import type { PropsWithChildren } from 'react';
@@ -23,7 +21,6 @@ const _hover: SystemStyleObject = {
 
 export const CanvasEntityGroupList = memo(({ isSelected, type, children }: Props) => {
   const title = useEntityTypeTitle(type);
-  const informationalPopoverFeature = useEntityTypeInformationalPopover(type);
   const collapse = useBoolean(true);
   const canMergeVisible = useMemo(() => type === 'raster_layer' || type === 'inpaint_mask', [type]);
   const canHideAll = useMemo(() => type !== 'reference_image', [type]);
@@ -50,30 +47,15 @@ export const CanvasEntityGroupList = memo(({ isSelected, type, children }: Props
             transitionProperty="common"
             transitionDuration="fast"
           />
-          {informationalPopoverFeature ? (
-            <InformationalPopover feature={informationalPopoverFeature}>
-              <Text
-                fontWeight="semibold"
-                color={isSelected ? 'base.200' : 'base.500'}
-                userSelect="none"
-                transitionProperty="common"
-                transitionDuration="fast"
-              >
-                {title}
-              </Text>
-            </InformationalPopover>
-          ) : (
-            <Text
-              fontWeight="semibold"
-              color={isSelected ? 'base.200' : 'base.500'}
-              userSelect="none"
-              transitionProperty="common"
-              transitionDuration="fast"
-            >
-              {title}
-            </Text>
-          )}
-
+          <Text
+            fontWeight="semibold"
+            color={isSelected ? 'base.200' : 'base.500'}
+            userSelect="none"
+            transitionProperty="common"
+            transitionDuration="fast"
+          >
+            {title}
+          </Text>
           <Spacer />
         </Flex>
         {canMergeVisible && <CanvasEntityMergeVisibleButton type={type} />}

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

@@ -2,7 +2,6 @@ import { MenuItem } from '@invoke-ai/ui-library';
 import { useEntityAdapterSafe } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import { useCopyLayerToClipboard } from 'features/controlLayers/hooks/useCopyLayerToClipboard';
-import { useEntityIsEmpty } from 'features/controlLayers/hooks/useEntityIsEmpty';
 import { useIsEntityInteractable } from 'features/controlLayers/hooks/useEntityIsInteractable';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -13,7 +12,6 @@ export const CanvasEntityMenuItemsCopyToClipboard = memo(() => {
   const entityIdentifier = useEntityIdentifierContext();
   const adapter = useEntityAdapterSafe(entityIdentifier);
   const isInteractable = useIsEntityInteractable(entityIdentifier);
-  const isEmpty = useEntityIsEmpty(entityIdentifier);
   const copyLayerToClipboard = useCopyLayerToClipboard();
 
   const onClick = useCallback(() => {
@@ -21,8 +19,8 @@ export const CanvasEntityMenuItemsCopyToClipboard = memo(() => {
   }, [copyLayerToClipboard, adapter]);
 
   return (
-    <MenuItem onClick={onClick} icon={<PiCopyBold />} isDisabled={!isInteractable || isEmpty}>
-      {t('common.clipboard')}
+    <MenuItem onClick={onClick} icon={<PiCopyBold />} isDisabled={!isInteractable}>
+      {t('controlLayers.copyToClipboard')}
     </MenuItem>
   );
 });

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

@@ -3,7 +3,7 @@ import { useEntityIdentifierContext } from 'features/controlLayers/contexts/Enti
 import { useEntityFilter } from 'features/controlLayers/hooks/useEntityFilter';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiShootingStarFill } from 'react-icons/pi';
+import { PiShootingStarBold } from 'react-icons/pi';
 
 export const CanvasEntityMenuItemsFilter = memo(() => {
   const { t } = useTranslation();
@@ -11,7 +11,7 @@ export const CanvasEntityMenuItemsFilter = memo(() => {
   const filter = useEntityFilter(entityIdentifier);
 
   return (
-    <MenuItem onClick={filter.start} icon={<PiShootingStarFill />} isDisabled={filter.isDisabled}>
+    <MenuItem onClick={filter.start} icon={<PiShootingStarBold />} isDisabled={filter.isDisabled}>
       {t('controlLayers.filter.filter')}
     </MenuItem>
   );

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

@@ -3,18 +3,18 @@ import { useEntityIdentifierContext } from 'features/controlLayers/contexts/Enti
 import { useEntitySegmentAnything } from 'features/controlLayers/hooks/useEntitySegmentAnything';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiShapesFill } from 'react-icons/pi';
+import { PiMaskHappyBold } from 'react-icons/pi';
 
-export const CanvasEntityMenuItemsSelectObject = memo(() => {
+export const CanvasEntityMenuItemsSegment = memo(() => {
   const { t } = useTranslation();
   const entityIdentifier = useEntityIdentifierContext();
   const segmentAnything = useEntitySegmentAnything(entityIdentifier);
 
   return (
-    <MenuItem onClick={segmentAnything.start} icon={<PiShapesFill />} isDisabled={segmentAnything.isDisabled}>
-      {t('controlLayers.selectObject.selectObject')}
+    <MenuItem onClick={segmentAnything.start} icon={<PiMaskHappyBold />} isDisabled={segmentAnything.isDisabled}>
+      {t('controlLayers.segment.autoMask')}
     </MenuItem>
   );
 });
 
-CanvasEntityMenuItemsSelectObject.displayName = 'CanvasEntityMenuItemsSelectObject';
+CanvasEntityMenuItemsSegment.displayName = 'CanvasEntityMenuItemsSegment';

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

@@ -24,9 +24,7 @@ import {
   selectEntityOrThrow,
 } from 'features/controlLayers/store/selectors';
 import type {
-  CanvasControlLayerState,
   CanvasEntityIdentifier,
-  CanvasInpaintMaskState,
   CanvasRasterLayerState,
   CanvasRegionalGuidanceState,
   ControlNetConfig,
@@ -46,8 +44,6 @@ import { useCallback } from 'react';
 import { modelConfigsAdapterSelectors, selectModelConfigsQuery } from 'services/api/endpoints/models';
 import type { ControlNetModelConfig, ImageDTO, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
 import { isControlNetOrT2IAdapterModelConfig, isIPAdapterModelConfig } from 'services/api/types';
-import type { Equals } from 'tsafe';
-import { assert } from 'tsafe';
 
 export const selectDefaultControlAdapter = createSelector(
   selectModelConfigsQuery,
@@ -128,60 +124,6 @@ export const useNewRasterLayerFromImage = () => {
   return func;
 };
 
-export const useNewControlLayerFromImage = () => {
-  const dispatch = useAppDispatch();
-  const bboxRect = useAppSelector(selectBboxRect);
-  const func = useCallback(
-    (imageDTO: ImageDTO) => {
-      const imageObject = imageDTOToImageObject(imageDTO);
-      const overrides: Partial<CanvasControlLayerState> = {
-        position: { x: bboxRect.x, y: bboxRect.y },
-        objects: [imageObject],
-      };
-      dispatch(controlLayerAdded({ overrides, isSelected: true }));
-    },
-    [bboxRect.x, bboxRect.y, dispatch]
-  );
-
-  return func;
-};
-
-export const useNewInpaintMaskFromImage = () => {
-  const dispatch = useAppDispatch();
-  const bboxRect = useAppSelector(selectBboxRect);
-  const func = useCallback(
-    (imageDTO: ImageDTO) => {
-      const imageObject = imageDTOToImageObject(imageDTO);
-      const overrides: Partial<CanvasInpaintMaskState> = {
-        position: { x: bboxRect.x, y: bboxRect.y },
-        objects: [imageObject],
-      };
-      dispatch(inpaintMaskAdded({ overrides, isSelected: true }));
-    },
-    [bboxRect.x, bboxRect.y, dispatch]
-  );
-
-  return func;
-};
-
-export const useNewRegionalGuidanceFromImage = () => {
-  const dispatch = useAppDispatch();
-  const bboxRect = useAppSelector(selectBboxRect);
-  const func = useCallback(
-    (imageDTO: ImageDTO) => {
-      const imageObject = imageDTOToImageObject(imageDTO);
-      const overrides: Partial<CanvasRegionalGuidanceState> = {
-        position: { x: bboxRect.x, y: bboxRect.y },
-        objects: [imageObject],
-      };
-      dispatch(rgAdded({ overrides, isSelected: true }));
-    },
-    [bboxRect.x, bboxRect.y, dispatch]
-  );
-
-  return func;
-};
-
 /**
  * Returns a function that adds a new canvas with the given image as the initial image, replicating the img2img flow:
  * - Reset the canvas
@@ -196,31 +138,18 @@ export const useNewCanvasFromImage = () => {
   const bboxRect = useAppSelector(selectBboxRect);
   const base = useAppSelector(selectBboxModelBase);
   const func = useCallback(
-    (imageDTO: ImageDTO, type: CanvasRasterLayerState['type'] | CanvasControlLayerState['type']) => {
+    (imageDTO: ImageDTO) => {
       // Calculate the new bbox dimensions to fit the image's aspect ratio at the optimal size
       const ratio = imageDTO.width / imageDTO.height;
       const optimalDimension = getOptimalDimension(base);
       const { width, height } = calculateNewSize(ratio, optimalDimension ** 2, base);
 
       // The overrides need to include the layer's ID so we can transform the layer it is initialized
-      let overrides: Partial<CanvasRasterLayerState> | Partial<CanvasControlLayerState>;
-
-      if (type === 'raster_layer') {
-        overrides = {
-          id: getPrefixedId('raster_layer'),
-          position: { x: bboxRect.x, y: bboxRect.y },
-          objects: [imageDTOToImageObject(imageDTO)],
-        } satisfies Partial<CanvasRasterLayerState>;
-      } else if (type === 'control_layer') {
-        overrides = {
-          id: getPrefixedId('control_layer'),
-          position: { x: bboxRect.x, y: bboxRect.y },
-          objects: [imageDTOToImageObject(imageDTO)],
-        } satisfies Partial<CanvasControlLayerState>;
-      } else {
-        // Catch unhandled types
-        assert<Equals<typeof type, never>>(false);
-      }
+      const overrides = {
+        id: getPrefixedId('raster_layer'),
+        position: { x: bboxRect.x, y: bboxRect.y },
+        objects: [imageDTOToImageObject(imageDTO)],
+      } satisfies Partial<CanvasRasterLayerState>;
 
       CanvasEntityAdapterBase.registerInitCallback(async (adapter) => {
         // Skip the callback if the adapter is not the one we are creating
@@ -237,16 +166,7 @@ export const useNewCanvasFromImage = () => {
       dispatch(canvasReset());
       // The `bboxChangedFromCanvas` reducer does no validation! Careful!
       dispatch(bboxChangedFromCanvas({ x: 0, y: 0, width, height }));
-
-      // The type casts are safe because the type is checked above
-      if (type === 'raster_layer') {
-        dispatch(rasterLayerAdded({ overrides: overrides as Partial<CanvasRasterLayerState>, isSelected: true }));
-      } else if (type === 'control_layer') {
-        dispatch(controlLayerAdded({ overrides: overrides as Partial<CanvasControlLayerState>, isSelected: true }));
-      } else {
-        // Catch unhandled types
-        assert<Equals<typeof type, never>>(false);
-      }
+      dispatch(rasterLayerAdded({ overrides, isSelected: true }));
     },
     [base, bboxRect.x, bboxRect.y, dispatch]
   );

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

@@ -1,4 +1,3 @@
-import { logger } from 'app/logging/logger';
 import type { CanvasEntityAdapterControlLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterControlLayer';
 import type { CanvasEntityAdapterInpaintMask } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterInpaintMask';
 import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterRasterLayer';
@@ -8,9 +7,6 @@ import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard';
 import { toast } from 'features/toast/toast';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
-import { serializeError } from 'serialize-error';
-
-const log = logger('canvas');
 
 export const useCopyLayerToClipboard = () => {
   const { t } = useTranslation();
@@ -30,13 +26,11 @@ export const useCopyLayerToClipboard = () => {
         const canvas = adapter.getCanvas();
         const blob = await canvasToBlob(canvas);
         copyBlobToClipboard(blob);
-        log.trace('Layer copied to clipboard');
         toast({
           status: 'info',
           title: t('toast.layerCopiedToClipboard'),
         });
       } catch (error) {
-        log.error({ error: serializeError(error) }, 'Problem copying layer to clipboard');
         toast({
           status: 'error',
           title: t('toast.problemCopyingLayer'),

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

@@ -5,13 +5,11 @@ import { useEntityAdapterSafe } from 'features/controlLayers/contexts/EntityAdap
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { isFilterableEntityIdentifier } from 'features/controlLayers/store/types';
-import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { useCallback, useMemo } from 'react';
 
 export const useEntityFilter = (entityIdentifier: CanvasEntityIdentifier | null) => {
   const canvasManager = useCanvasManager();
   const adapter = useEntityAdapterSafe(entityIdentifier);
-  const imageViewer = useImageViewer();
   const isBusy = useCanvasIsBusy();
   const isInteractable = useStore(adapter?.$isInteractable ?? $false);
   const isEmpty = useStore(adapter?.$isEmpty ?? $false);
@@ -52,9 +50,8 @@ export const useEntityFilter = (entityIdentifier: CanvasEntityIdentifier | null)
     if (!adapter) {
       return;
     }
-    imageViewer.close();
     adapter.filterer.start();
-  }, [isDisabled, entityIdentifier, canvasManager, imageViewer]);
+  }, [isDisabled, entityIdentifier, canvasManager]);
 
   return { isDisabled, start } as const;
 };

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

@@ -1,11 +0,0 @@
-import { useAppSelector } from 'app/store/storeHooks';
-import { buildSelectHasObjects } from 'features/controlLayers/store/selectors';
-import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
-import { useMemo } from 'react';
-
-export const useEntityIsEmpty = (entityIdentifier: CanvasEntityIdentifier) => {
-  const selectHasObjects = useMemo(() => buildSelectHasObjects(entityIdentifier), [entityIdentifier]);
-  const hasObjects = useAppSelector(selectHasObjects);
-
-  return !hasObjects;
-};

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

@@ -5,13 +5,11 @@ import { useEntityAdapterSafe } from 'features/controlLayers/contexts/EntityAdap
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { isSegmentableEntityIdentifier } from 'features/controlLayers/store/types';
-import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { useCallback, useMemo } from 'react';
 
 export const useEntitySegmentAnything = (entityIdentifier: CanvasEntityIdentifier | null) => {
   const canvasManager = useCanvasManager();
   const adapter = useEntityAdapterSafe(entityIdentifier);
-  const imageViewer = useImageViewer();
   const isBusy = useCanvasIsBusy();
   const isInteractable = useStore(adapter?.$isInteractable ?? $false);
   const isEmpty = useStore(adapter?.$isEmpty ?? $false);
@@ -52,9 +50,8 @@ export const useEntitySegmentAnything = (entityIdentifier: CanvasEntityIdentifie
     if (!adapter) {
       return;
     }
-    imageViewer.close();
     adapter.segmentAnything.start();
-  }, [isDisabled, entityIdentifier, canvasManager, imageViewer]);
+  }, [isDisabled, entityIdentifier, canvasManager]);
 
   return { isDisabled, start } as const;
 };

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

@@ -5,13 +5,11 @@ import { useEntityAdapterSafe } from 'features/controlLayers/contexts/EntityAdap
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
 import { isTransformableEntityIdentifier } from 'features/controlLayers/store/types';
-import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { useCallback, useMemo } from 'react';
 
 export const useEntityTransform = (entityIdentifier: CanvasEntityIdentifier | null) => {
   const canvasManager = useCanvasManager();
   const adapter = useEntityAdapterSafe(entityIdentifier);
-  const imageViewer = useImageViewer();
   const isBusy = useCanvasIsBusy();
   const isInteractable = useStore(adapter?.$isInteractable ?? $false);
   const isEmpty = useStore(adapter?.$isEmpty ?? $false);
@@ -52,9 +50,8 @@ export const useEntityTransform = (entityIdentifier: CanvasEntityIdentifier | nu
     if (!adapter) {
       return;
     }
-    imageViewer.close();
     await adapter.transformer.startTransform();
-  }, [isDisabled, entityIdentifier, canvasManager, imageViewer]);
+  }, [isDisabled, entityIdentifier, canvasManager]);
 
   const fitToBbox = useCallback(async () => {
     if (isDisabled) {
@@ -70,11 +67,10 @@ export const useEntityTransform = (entityIdentifier: CanvasEntityIdentifier | nu
     if (!adapter) {
       return;
     }
-    imageViewer.close();
     await adapter.transformer.startTransform({ silent: true });
     adapter.transformer.fitToBboxContain();
     await adapter.transformer.applyTransform();
-  }, [canvasManager, entityIdentifier, imageViewer, isDisabled]);
+  }, [canvasManager, entityIdentifier, isDisabled]);
 
   return { isDisabled, start, fitToBbox } as const;
 };

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

@@ -1,25 +0,0 @@
-import type { Feature } from 'common/components/InformationalPopover/constants';
-import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
-import { useMemo } from 'react';
-
-export const useEntityTypeInformationalPopover = (type: CanvasEntityIdentifier['type']): Feature | undefined => {
-  const feature = useMemo(() => {
-    switch (type) {
-      case 'control_layer':
-        return 'controlNet';
-      case 'inpaint_mask':
-        return 'inpainting';
-      case 'raster_layer':
-        return 'rasterLayer';
-      case 'regional_guidance':
-        return 'regionalGuidanceAndReferenceImage';
-      case 'reference_image':
-        return 'globalReferenceImage';
-
-      default:
-        return undefined;
-    }
-  }, [type]);
-
-  return feature;
-};

invokeai/frontend/web/src/features/controlLayers/konva/CanvasEntity/CanvasEntityFilterer.ts

@@ -1,36 +1,27 @@
-import { deepClone } from 'common/util/deepClone';
 import { withResult, withResultAsync } from 'common/util/result';
 import type { CanvasEntityAdapterControlLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterControlLayer';
 import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterRasterLayer';
 import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
 import { CanvasModuleBase } from 'features/controlLayers/konva/CanvasModuleBase';
-import { CanvasObjectImage } from 'features/controlLayers/konva/CanvasObject/CanvasObjectImage';
-import { addCoords, getKonvaNodeDebugAttrs, getPrefixedId } from 'features/controlLayers/konva/util';
+import { getPrefixedId } from 'features/controlLayers/konva/util';
 import { selectAutoProcess } from 'features/controlLayers/store/canvasSettingsSlice';
 import type { FilterConfig } from 'features/controlLayers/store/filters';
 import { getFilterForModel, IMAGE_FILTERS } from 'features/controlLayers/store/filters';
-import type { CanvasEntityType, CanvasImageState } from 'features/controlLayers/store/types';
+import type { CanvasImageState } from 'features/controlLayers/store/types';
 import { imageDTOToImageObject } from 'features/controlLayers/store/util';
-import Konva from 'konva';
 import { debounce } from 'lodash-es';
-import { atom, computed } from 'nanostores';
+import { atom } from 'nanostores';
 import type { Logger } from 'roarr';
 import { serializeError } from 'serialize-error';
 import { buildSelectModelConfig } from 'services/api/hooks/modelsByType';
 import { isControlNetOrT2IAdapterModelConfig } from 'services/api/types';
-import stableHash from 'stable-hash';
-import type { Equals } from 'tsafe';
-import { assert } from 'tsafe';
 
 type CanvasEntityFiltererConfig = {
-  /**
-   * The debounce time in milliseconds for processing the filter.
-   */
-  PROCESS_DEBOUNCE_MS: number;
+  processDebounceMs: number;
 };
 
 const DEFAULT_CONFIG: CanvasEntityFiltererConfig = {
-  PROCESS_DEBOUNCE_MS: 1000,
+  processDebounceMs: 1000,
 };
 
 export class CanvasEntityFilterer extends CanvasModuleBase {
@@ -41,65 +32,20 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
   readonly manager: CanvasManager;
   readonly log: Logger;
 
-  config: CanvasEntityFiltererConfig = DEFAULT_CONFIG;
-
+  imageState: CanvasImageState | null = null;
   subscriptions = new Set<() => void>();
+  config: CanvasEntityFiltererConfig = DEFAULT_CONFIG;
 
   /**
    * The AbortController used to cancel the filter processing.
    */
   abortController: AbortController | null = null;
 
-  /**
-   * Whether the module is currently filtering an image.
-   */
   $isFiltering = atom<boolean>(false);
-  /**
-   * The hash of the last processed config. This is used to prevent re-processing the same config.
-   */
-  $lastProcessedHash = atom<string>('');
-
-  /**
-   * Whether the module is currently processing the filter.
-   */
+  $hasProcessed = atom<boolean>(false);
   $isProcessing = atom<boolean>(false);
-
-  /**
-   * The config for the filter.
-   */
   $filterConfig = atom<FilterConfig>(IMAGE_FILTERS.canny_edge_detection.buildDefaults());
 
-  /**
-   * The initial filter config, used to reset the filter config.
-   */
-  $initialFilterConfig = atom<FilterConfig | null>(null);
-
-  /**
-   * The ephemeral image state of the filtered image.
-   */
-  $imageState = atom<CanvasImageState | null>(null);
-
-  /**
-   * Whether the module has an image state. This is a computed value based on $imageState.
-   */
-  $hasImageState = computed(this.$imageState, (imageState) => imageState !== null);
-  /**
-   * The filtered image object module, if it exists.
-   */
-  imageModule: CanvasObjectImage | null = null;
-
-  /**
-   * The Konva nodes for the module.
-   */
-  konva: {
-    /**
-     * The main Konva group node for the module. This is added to the parent layer on start, and removed on teardown.
-     */
-    group: Konva.Group;
-  };
-
-  KONVA_GROUP_NAME = `${this.type}:group`;
-
   constructor(parent: CanvasEntityAdapterRasterLayer | CanvasEntityAdapterControlLayer) {
     super();
     this.id = getPrefixedId(this.type);
@@ -109,17 +55,9 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
     this.log = this.manager.buildLogger(this);
 
     this.log.debug('Creating filter module');
-
-    this.konva = {
-      group: new Konva.Group({ name: this.KONVA_GROUP_NAME }),
-    };
   }
 
-  /**
-   * Adds event listeners needed while filtering the entity.
-   */
   subscribe = () => {
-    // As the filter config changes, process the filter
     this.subscriptions.add(
       this.$filterConfig.listen(() => {
         if (this.manager.stateApi.getSettings().autoProcess && this.$isFiltering.get()) {
@@ -127,7 +65,6 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
         }
       })
     );
-    // When auto-process is enabled, process the filter
     this.subscriptions.add(
       this.manager.stateApi.createStoreSubscription(selectAutoProcess, (autoProcess) => {
         if (autoProcess && this.$isFiltering.get()) {
@@ -137,18 +74,11 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
     );
   };
 
-  /**
-   * Removes event listeners used while filtering the entity.
-   */
   unsubscribe = () => {
     this.subscriptions.forEach((unsubscribe) => unsubscribe());
     this.subscriptions.clear();
   };
 
-  /**
-   * Starts the filter module.
-   * @param config The filter config to start with. If omitted, the default filter config is used.
-   */
   start = (config?: FilterConfig) => {
     const filteringAdapter = this.manager.stateApi.$filteringAdapter.get();
     if (filteringAdapter) {
@@ -158,57 +88,30 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
 
     this.log.trace('Initializing filter');
 
-    // Reset any previous state
-    this.resetEphemeralState();
-    this.$isFiltering.set(true);
-
-    // Update the konva group's position to match the parent entity
-    const pixelRect = this.parent.transformer.$pixelRect.get();
-    const position = addCoords(this.parent.state.position, pixelRect);
-    this.konva.group.setAttrs(position);
-
-    // Add the group to the parent layer
-    this.parent.konva.layer.add(this.konva.group);
-
-    if (config) {
-      // If a config is provided, use it
-      this.$filterConfig.set(config);
-      this.$initialFilterConfig.set(config);
-    } else {
-      this.$filterConfig.set(this.createInitialFilterConfig());
-    }
-
-    this.$initialFilterConfig.set(this.$filterConfig.get());
-
     this.subscribe();
 
-    this.manager.stateApi.$filteringAdapter.set(this.parent);
-
-    if (this.manager.stateApi.getSettings().autoProcess) {
-      this.processImmediate();
-    }
-  };
-
-  createInitialFilterConfig = (): FilterConfig => {
-    if (this.parent.type === 'control_layer_adapter' && this.parent.state.controlAdapter.model) {
+    if (config) {
+      this.$filterConfig.set(config);
+    } else if (this.parent.type === 'control_layer_adapter' && this.parent.state.controlAdapter.model) {
       // If the parent is a control layer adapter, we should check if the model has a default filter and set it if so
       const selectModelConfig = buildSelectModelConfig(
         this.parent.state.controlAdapter.model.key,
         isControlNetOrT2IAdapterModelConfig
       );
       const modelConfig = this.manager.stateApi.runSelector(selectModelConfig);
-      // This always returns a filter
       const filter = getFilterForModel(modelConfig);
-      return filter.buildDefaults();
+      this.$filterConfig.set(filter.buildDefaults());
     } else {
-      // Otherwise, used the default filter
-      return IMAGE_FILTERS.canny_edge_detection.buildDefaults();
+      // Otherwise, set the default filter
+      this.$filterConfig.set(IMAGE_FILTERS.canny_edge_detection.buildDefaults());
+    }
+    this.$isFiltering.set(true);
+    this.manager.stateApi.$filteringAdapter.set(this.parent);
+    if (this.manager.stateApi.getSettings().autoProcess) {
+      this.processImmediate();
     }
   };
 
-  /**
-   * Processes the filter, updating the module's state and rendering the filtered image.
-   */
   processImmediate = async () => {
     const config = this.$filterConfig.get();
     const filterData = IMAGE_FILTERS[config.type];
@@ -220,12 +123,6 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
       return;
     }
 
-    const hash = stableHash({ config });
-    if (hash === this.$lastProcessedHash.get()) {
-      this.log.trace('Already processed config');
-      return;
-    }
-
     this.log.trace({ config }, 'Processing filter');
     const rect = this.parent.transformer.getRelativeRect();
 
@@ -259,181 +156,91 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
       this.manager.stateApi.runGraphAndReturnImageOutput({
         graph,
         outputNodeId,
+        // The filter graph should always be prepended to the queue so it's processed ASAP.
         prepend: true,
+        /**
+         * The filter node may need to download a large model. Currently, the models required by the filter nodes are
+         * downloaded just-in-time, as required by the filter. If we use a timeout here, we might get into a catch-22
+         * where the filter node is waiting for the model to download, but the download gets canceled if the filter
+         * node times out.
+         *
+         * (I suspect the model download will actually _not_ be canceled if the graph is canceled, but let's not chance it!)
+         *
+         * TODO(psyche): Figure out a better way to handle this. Probably need to download the models ahead of time.
+         */
+        // timeout: 5000,
+        /**
+         * The filter node should be able to cancel the request if it's taking too long. This will cancel the graph's
+         * queue item and clear any event listeners on the request.
+         */
         signal: controller.signal,
       })
     );
-
-    // If there is an error, log it and bail out of this processing run
     if (filterResult.isErr()) {
-      this.log.error({ error: serializeError(filterResult.error) }, 'Error filtering');
+      this.log.error({ error: serializeError(filterResult.error) }, 'Error processing filter');
       this.$isProcessing.set(false);
-      // Clean up the abort controller as needed
-      if (!this.abortController.signal.aborted) {
-        this.abortController.abort();
-      }
       this.abortController = null;
       return;
     }
 
-    this.log.trace({ imageDTO: filterResult.value }, 'Filtered');
+    this.log.trace({ imageDTO: filterResult.value }, 'Filter processed');
+    this.imageState = imageDTOToImageObject(filterResult.value);
 
-    // Prepare the ephemeral image state
-    const imageState = imageDTOToImageObject(filterResult.value);
-    this.$imageState.set(imageState);
-
-    // Destroy any existing masked image and create a new one
-    if (this.imageModule) {
-      this.imageModule.destroy();
-    }
-
-    this.imageModule = new CanvasObjectImage(imageState, this);
-
-    // Force update the masked image - after awaiting, the image will be rendered (in memory)
-    await this.imageModule.update(imageState, true);
-
-    this.konva.group.add(this.imageModule.konva.group);
-
-    // The porcessing is complete, set can set the last processed hash and isProcessing to false
-    this.$lastProcessedHash.set(hash);
+    await this.parent.bufferRenderer.setBuffer(this.imageState, true);
 
     this.$isProcessing.set(false);
-
-    // Clean up the abort controller as needed
-    if (!this.abortController.signal.aborted) {
-      this.abortController.abort();
-    }
-
+    this.$hasProcessed.set(true);
     this.abortController = null;
   };
 
-  /**
-   * Debounced version of processImmediate.
-   */
-  process = debounce(this.processImmediate, this.config.PROCESS_DEBOUNCE_MS);
+  process = debounce(this.processImmediate, this.config.processDebounceMs);
 
-  /**
-   * Applies the filter image to the entity, replacing the entity's objects with the filtered image.
-   */
   apply = () => {
-    const filteredImageObjectState = this.$imageState.get();
-    if (!filteredImageObjectState) {
+    const imageState = this.imageState;
+    if (!imageState) {
       this.log.warn('No image state to apply filter to');
       return;
     }
-    this.log.trace('Applying');
-
-    // Have the parent adopt the image module - this prevents a flash of the original layer content before the filtered
-    // image is rendered
-    if (this.imageModule) {
-      this.parent.renderer.adoptObjectRenderer(this.imageModule);
-    }
-
-    // Rasterize the entity, replacing the objects with the masked image
+    this.log.trace('Applying filter');
+    this.parent.bufferRenderer.commitBuffer();
     const rect = this.parent.transformer.getRelativeRect();
     this.manager.stateApi.rasterizeEntity({
       entityIdentifier: this.parent.entityIdentifier,
-      imageObject: filteredImageObjectState,
+      imageObject: imageState,
       position: {
         x: Math.round(rect.x),
         y: Math.round(rect.y),
       },
       replaceObjects: true,
     });
-
-    // Final cleanup and teardown, returning user to main canvas UI
-    this.resetEphemeralState();
-    this.teardown();
-  };
-
-  /**
-   * Saves the filtered image as a new entity of the given type.
-   * @param type The type of entity to save the filtered image as.
-   */
-  saveAs = (type: Exclude<CanvasEntityType, 'reference_image'>) => {
-    const imageState = this.$imageState.get();
-    if (!imageState) {
-      this.log.warn('No image state to apply filter to');
-      return;
-    }
-    this.log.trace(`Saving as ${type}`);
-
-    const rect = this.parent.transformer.getRelativeRect();
-    const arg = {
-      overrides: {
-        objects: [imageState],
-        position: {
-          x: Math.round(rect.x),
-          y: Math.round(rect.y),
-        },
-      },
-      isSelected: true,
-    };
-
-    switch (type) {
-      case 'raster_layer':
-        this.manager.stateApi.addRasterLayer(arg);
-        break;
-      case 'control_layer':
-        this.manager.stateApi.addControlLayer(arg);
-        break;
-      case 'inpaint_mask':
-        this.manager.stateApi.addInpaintMask(arg);
-        break;
-      case 'regional_guidance':
-        this.manager.stateApi.addRegionalGuidance(arg);
-        break;
-      default:
-        assert<Equals<typeof type, never>>(false);
-    }
-
-    // Final cleanup and teardown, returning user to main canvas UI
-    this.resetEphemeralState();
-    this.teardown();
-  };
-
-  resetEphemeralState = () => {
-    // First we need to bail out of any processing
-    if (this.abortController && !this.abortController.signal.aborted) {
-      this.abortController.abort();
-    }
-    this.abortController = null;
-
-    // If the image module exists, and is a child of the group, destroy it. It might not be a child of the group if
-    // the user has applied the filter and the image has been adopted by the parent entity.
-    if (this.imageModule && this.imageModule.konva.group.parent === this.konva.group) {
-      this.imageModule.destroy();
-      this.imageModule = null;
-    }
-    const initialFilterConfig = this.$initialFilterConfig.get() ?? this.createInitialFilterConfig();
-    this.$filterConfig.set(initialFilterConfig);
-    this.$imageState.set(null);
-    this.$lastProcessedHash.set('');
-    this.$isProcessing.set(false);
-  };
-
-  teardown = () => {
-    this.$initialFilterConfig.set(null);
-    this.konva.group.remove();
+    this.imageState = null;
     this.unsubscribe();
     this.$isFiltering.set(false);
+    this.$hasProcessed.set(false);
     this.manager.stateApi.$filteringAdapter.set(null);
   };
 
-  /**
-   * Resets the module (e.g. remove all points and the mask image).
-   *
-   * Does not cancel or otherwise complete the segmenting process.
-   */
   reset = () => {
-    this.log.trace('Resetting');
-    this.resetEphemeralState();
+    this.log.trace('Resetting filter');
+
+    this.abortController?.abort();
+    this.abortController = null;
+    this.parent.bufferRenderer.clearBuffer();
+    this.parent.transformer.updatePosition();
+    this.parent.renderer.syncKonvaCache(true);
+    this.imageState = null;
+    this.$hasProcessed.set(false);
   };
 
   cancel = () => {
-    this.log.trace('Canceling');
-    this.resetEphemeralState();
-    this.teardown();
+    this.log.trace('Cancelling filter');
+
+    this.reset();
+    this.unsubscribe();
+    this.$isProcessing.set(false);
+    this.$isFiltering.set(false);
+    this.$hasProcessed.set(false);
+    this.manager.stateApi.$filteringAdapter.set(null);
   };
 
   repr = () => {
@@ -441,14 +248,11 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
       id: this.id,
       type: this.type,
       path: this.path,
-      parent: this.parent.id,
       config: this.config,
-      imageState: deepClone(this.$imageState.get()),
       $isFiltering: this.$isFiltering.get(),
-      $lastProcessedHash: this.$lastProcessedHash.get(),
+      $hasProcessed: this.$hasProcessed.get(),
       $isProcessing: this.$isProcessing.get(),
       $filterConfig: this.$filterConfig.get(),
-      konva: { group: getKonvaNodeDebugAttrs(this.konva.group) },
     };
   };
 
@@ -459,6 +263,5 @@ export class CanvasEntityFilterer extends CanvasModuleBase {
     }
     this.abortController = null;
     this.unsubscribe();
-    this.konva.group.destroy();
   };
 }

invokeai/frontend/web/src/features/controlLayers/konva/CanvasObject/CanvasObjectImage.ts

@@ -1,7 +1,6 @@
 import { Mutex } from 'async-mutex';
 import { deepClone } from 'common/util/deepClone';
 import type { CanvasEntityBufferObjectRenderer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityBufferObjectRenderer';
-import type { CanvasEntityFilterer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityFilterer';
 import type { CanvasEntityObjectRenderer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityObjectRenderer';
 import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
 import { CanvasModuleBase } from 'features/controlLayers/konva/CanvasModuleBase';
@@ -22,8 +21,7 @@ export class CanvasObjectImage extends CanvasModuleBase {
     | CanvasEntityObjectRenderer
     | CanvasEntityBufferObjectRenderer
     | CanvasStagingAreaModule
-    | CanvasSegmentAnythingModule
-    | CanvasEntityFilterer;
+    | CanvasSegmentAnythingModule;
   readonly manager: CanvasManager;
   readonly log: Logger;
 
@@ -45,7 +43,6 @@ export class CanvasObjectImage extends CanvasModuleBase {
       | CanvasEntityBufferObjectRenderer
       | CanvasStagingAreaModule
       | CanvasSegmentAnythingModule
-      | CanvasEntityFilterer
   ) {
     super();
     this.id = state.id;

invokeai/frontend/web/src/features/controlLayers/konva/CanvasSegmentAnythingModule.ts

@@ -6,22 +6,15 @@ import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konv
 import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
 import { CanvasModuleBase } from 'features/controlLayers/konva/CanvasModuleBase';
 import { CanvasObjectImage } from 'features/controlLayers/konva/CanvasObject/CanvasObjectImage';
-import {
-  addCoords,
-  getKonvaNodeDebugAttrs,
-  getPrefixedId,
-  offsetCoord,
-  roundCoord,
-} from 'features/controlLayers/konva/util';
+import { addCoords, getKonvaNodeDebugAttrs, getPrefixedId, offsetCoord } from 'features/controlLayers/konva/util';
 import { selectAutoProcess } from 'features/controlLayers/store/canvasSettingsSlice';
 import type {
-  CanvasEntityType,
   CanvasImageState,
   Coordinate,
   RgbaColor,
+  SAMPoint,
   SAMPointLabel,
   SAMPointLabelString,
-  SAMPointWithId,
 } from 'features/controlLayers/store/types';
 import { SAM_POINT_LABEL_NUMBER_TO_STRING } from 'features/controlLayers/store/types';
 import { imageDTOToImageObject } from 'features/controlLayers/store/util';
@@ -34,9 +27,6 @@ import { atom, computed } from 'nanostores';
 import type { Logger } from 'roarr';
 import { serializeError } from 'serialize-error';
 import type { ImageDTO } from 'services/api/types';
-import stableHash from 'stable-hash';
-import type { Equals } from 'tsafe';
-import { assert } from 'tsafe';
 
 type CanvasSegmentAnythingModuleConfig = {
   /**
@@ -80,7 +70,7 @@ const DEFAULT_CONFIG: CanvasSegmentAnythingModuleConfig = {
   SAM_POINT_FOREGROUND_COLOR: { r: 50, g: 255, b: 0, a: 1 }, // light green
   SAM_POINT_BACKGROUND_COLOR: { r: 255, g: 0, b: 50, a: 1 }, // red-ish
   SAM_POINT_NEUTRAL_COLOR: { r: 0, g: 225, b: 255, a: 1 }, // cyan
-  MASK_COLOR: { r: 0, g: 225, b: 255, a: 1 }, // cyan
+  MASK_COLOR: { r: 0, g: 200, b: 200, a: 0.5 }, // cyan with 50% opacity
   PROCESS_DEBOUNCE_MS: 1000,
 };
 
@@ -95,7 +85,6 @@ const DEFAULT_CONFIG: CanvasSegmentAnythingModuleConfig = {
 type SAMPointState = {
   id: string;
   label: SAMPointLabel;
-  coord: Coordinate;
   konva: {
     circle: Konva.Circle;
   };
@@ -114,7 +103,7 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
   subscriptions = new Set<() => void>();
 
   /**
-   * The AbortController used to cancel the segment processing.
+   * The AbortController used to cancel the filter processing.
    */
   abortController: AbortController | null = null;
 
@@ -124,9 +113,9 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
   $isSegmenting = atom<boolean>(false);
 
   /**
-   * The hash of the last processed points. This is used to prevent re-processing the same points.
+   * Whether the current set of points has been processed.
    */
-  $lastProcessedHash = atom<string>('');
+  $hasProcessed = atom<boolean>(false);
 
   /**
    * Whether the module is currently processing the points.
@@ -155,15 +144,10 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
   /**
    * The ephemeral image state of the processed image. Only used while segmenting.
    */
-  $imageState = atom<CanvasImageState | null>(null);
+  imageState: CanvasImageState | null = null;
 
   /**
-   * Whether the module has an image state. This is a computed value based on $imageState.
-   */
-  $hasImageState = computed(this.$imageState, (imageState) => imageState !== null);
-
-  /**
-   * The current input points. A listener is added to this atom to process the points when they change.
+   * The current input points.
    */
   $points = atom<SAMPointState[]>([]);
 
@@ -173,21 +157,16 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
   $hasPoints = computed(this.$points, (points) => points.length > 0);
 
   /**
-   * Whether the module should invert the mask image.
+   * The masked image object, if it exists.
    */
-  $invert = atom<boolean>(false);
-
-  /**
-   * The masked image object module, if it exists.
-   */
-  imageModule: CanvasObjectImage | null = null;
+  maskedImage: CanvasObjectImage | null = null;
 
   /**
    * The Konva nodes for the module.
    */
   konva: {
     /**
-     * The main Konva group node for the module. This is added to the parent layer on start, and removed on teardown.
+     * The main Konva group node for the module.
      */
     group: Konva.Group;
     /**
@@ -208,10 +187,6 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
      * It's rendered with a globalCompositeOperation of 'source-atop' to preview the mask as a semi-transparent overlay.
      */
     compositingRect: Konva.Rect;
-    /**
-     * A tween for pulsing the mask group's opacity.
-     */
-    maskTween: Konva.Tween | null;
   };
 
   KONVA_CIRCLE_NAME = `${this.type}:circle`;
@@ -234,7 +209,7 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
     this.konva = {
       group: new Konva.Group({ name: this.KONVA_GROUP_NAME }),
       pointGroup: new Konva.Group({ name: this.KONVA_POINT_GROUP_NAME }),
-      maskGroup: new Konva.Group({ name: this.KONVA_MASK_GROUP_NAME, opacity: 0.6 }),
+      maskGroup: new Konva.Group({ name: this.KONVA_MASK_GROUP_NAME }),
       compositingRect: new Konva.Rect({
         name: this.KONVA_COMPOSITING_RECT_NAME,
         fill: rgbaColorToString(this.config.MASK_COLOR),
@@ -244,7 +219,6 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
         perfectDrawEnabled: false,
         visible: false,
       }),
-      maskTween: null,
     };
 
     // Points should always be rendered above the mask group
@@ -276,12 +250,10 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
   createPoint(coord: Coordinate, label: SAMPointLabel): SAMPointState {
     const id = getPrefixedId('sam_point');
 
-    const roundedCoord = roundCoord(coord);
-
     const circle = new Konva.Circle({
       name: this.KONVA_CIRCLE_NAME,
-      x: roundedCoord.x,
-      y: roundedCoord.y,
+      x: Math.round(coord.x),
+      y: Math.round(coord.y),
       radius: this.manager.stage.unscale(this.config.SAM_POINT_RADIUS), // We will scale this as the stage scale changes
       fill: rgbaColorToString(this.getSAMPointColor(label)),
       stroke: rgbaColorToString(this.config.SAM_POINT_BORDER_COLOR),
@@ -298,18 +270,14 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
       if (this.$isDraggingPoint.get()) {
         return;
       }
-      if (e.evt.button !== 0) {
-        return;
-      }
       // This event should not bubble up to the parent, stage or any other nodes
       e.cancelBubble = true;
       circle.destroy();
-
-      const newPoints = this.$points.get().filter((point) => point.id !== id);
-      if (newPoints.length === 0) {
+      this.$points.set(this.$points.get().filter((point) => point.id !== id));
+      if (this.$points.get().length === 0) {
         this.resetEphemeralState();
       } else {
-        this.$points.set(newPoints);
+        this.$hasProcessed.set(false);
       }
     });
 
@@ -318,28 +286,25 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
     });
 
     circle.on('dragend', () => {
-      const roundedCoord = roundCoord(circle.position());
-
-      this.log.trace({ ...roundedCoord, label: SAM_POINT_LABEL_NUMBER_TO_STRING[label] }, 'Moved SAM point');
       this.$isDraggingPoint.set(false);
-
-      const newPoints = this.$points.get().map((point) => {
-        if (point.id === id) {
-          return { ...point, coord: roundedCoord };
-        }
-        return point;
-      });
-
-      this.$points.set(newPoints);
+      // Point has changed!
+      this.$hasProcessed.set(false);
+      this.$points.notify();
+      this.log.trace(
+        { x: Math.round(circle.x()), y: Math.round(circle.y()), label: SAM_POINT_LABEL_NUMBER_TO_STRING[label] },
+        'Moved SAM point'
+      );
     });
 
     this.konva.pointGroup.add(circle);
 
-    this.log.trace({ ...roundedCoord, label: SAM_POINT_LABEL_NUMBER_TO_STRING[label] }, 'Created SAM point');
+    this.log.trace(
+      { x: Math.round(circle.x()), y: Math.round(circle.y()), label: SAM_POINT_LABEL_NUMBER_TO_STRING[label] },
+      'Created SAM point'
+    );
 
     return {
       id,
-      coord: roundedCoord,
       label,
       konva: { circle },
     };
@@ -362,14 +327,14 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
   /**
    * Gets the SAM points in the format expected by the segment-anything API. The x and y values are rounded to integers.
    */
-  getSAMPoints = (): SAMPointWithId[] => {
-    const points: SAMPointWithId[] = [];
+  getSAMPoints = (): SAMPoint[] => {
+    const points: SAMPoint[] = [];
 
-    for (const { id, coord, label } of this.$points.get()) {
+    for (const { konva, label } of this.$points.get()) {
       points.push({
-        id,
-        x: coord.x,
-        y: coord.y,
+        // Pull out and round the x and y values from Konva
+        x: Math.round(konva.circle.x()),
+        y: Math.round(konva.circle.y()),
         label,
       });
     }
@@ -416,8 +381,10 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
 
     // Create a SAM point at the normalized position
     const point = this.createPoint(normalizedPoint, this.$pointType.get());
-    const newPoints = [...this.$points.get(), point];
-    this.$points.set(newPoints);
+    this.$points.set([...this.$points.get(), point]);
+
+    // Mark the module as having _not_ processed the points now that they have changed
+    this.$hasProcessed.set(false);
   };
 
   /**
@@ -454,20 +421,6 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
         if (points.length === 0) {
           return;
         }
-
-        if (this.manager.stateApi.getSettings().autoProcess) {
-          this.process();
-        }
-      })
-    );
-
-    // When the invert flag changes, process if autoProcess is enabled
-    this.subscriptions.add(
-      this.$invert.listen(() => {
-        if (this.$points.get().length === 0) {
-          return;
-        }
-
         if (this.manager.stateApi.getSettings().autoProcess) {
           this.process();
         }
@@ -480,7 +433,7 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
         if (this.$points.get().length === 0) {
           return;
         }
-        if (autoProcess) {
+        if (autoProcess && !this.$hasProcessed.get()) {
           this.process();
         }
       })
@@ -488,7 +441,7 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
   };
 
   /**
-   * Removes event listeners used while segmenting the entity.
+   * Adds event listeners needed while segmenting the entity.
    */
   unsubscribe = () => {
     this.subscriptions.forEach((unsubscribe) => unsubscribe());
@@ -547,14 +500,6 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
       return;
     }
 
-    const invert = this.$invert.get();
-
-    const hash = stableHash({ points, invert });
-    if (hash === this.$lastProcessedHash.get()) {
-      this.log.trace('Already processed points');
-      return;
-    }
-
     this.$isProcessing.set(true);
 
     this.log.trace({ points }, 'Segmenting');
@@ -576,7 +521,7 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
     this.abortController = controller;
 
     // Build the graph for segmenting the image, using the rasterized image DTO
-    const { graph, outputNodeId } = CanvasSegmentAnythingModule.buildGraph(rasterizeResult.value, points, invert);
+    const { graph, outputNodeId } = this.buildGraph(rasterizeResult.value);
 
     // Run the graph and get the segmented image output
     const segmentResult = await withResultAsync(() =>
@@ -603,56 +548,38 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
     this.log.trace({ imageDTO: segmentResult.value }, 'Segmented');
 
     // Prepare the ephemeral image state
-    const imageState = imageDTOToImageObject(segmentResult.value);
-    this.$imageState.set(imageState);
+    this.imageState = imageDTOToImageObject(segmentResult.value);
 
     // Destroy any existing masked image and create a new one
-    if (this.imageModule) {
-      this.imageModule.destroy();
+    if (this.maskedImage) {
+      this.maskedImage.destroy();
     }
-    if (this.konva.maskTween) {
-      this.konva.maskTween.destroy();
-      this.konva.maskTween = null;
-    }
-
-    this.imageModule = new CanvasObjectImage(imageState, this);
+    this.maskedImage = new CanvasObjectImage(this.imageState, this);
 
     // Force update the masked image - after awaiting, the image will be rendered (in memory)
-    await this.imageModule.update(imageState, true);
+    await this.maskedImage.update(this.imageState, true);
 
     // Update the compositing rect to match the image size
     this.konva.compositingRect.setAttrs({
-      width: imageState.image.width,
-      height: imageState.image.height,
+      width: this.imageState.image.width,
+      height: this.imageState.image.height,
       visible: true,
     });
 
     // Now we can add the masked image to the mask group. It will be rendered above the compositing rect, but should be
     // under it, so we will move the compositing rect to the top
-    this.konva.maskGroup.add(this.imageModule.konva.group);
+    this.konva.maskGroup.add(this.maskedImage.konva.group);
     this.konva.compositingRect.moveToTop();
 
     // Cache the group to ensure the mask is rendered correctly w/ opacity
     this.konva.maskGroup.cache();
 
-    // Create a pulsing tween
-    this.konva.maskTween = new Konva.Tween({
-      node: this.konva.maskGroup,
-      duration: 1,
-      opacity: 0.4, // oscillate between this value and pre-tween opacity
-      yoyo: true,
-      repeat: Infinity,
-      easing: Konva.Easings.EaseOut,
-    });
-
-    // Start the pulsing effect
-    this.konva.maskTween.play();
-
-    this.$lastProcessedHash.set(hash);
-
     // We are done processing (still segmenting though!)
     this.$isProcessing.set(false);
 
+    // The current points have been processed
+    this.$hasProcessed.set(true);
+
     // Clean up the abort controller as needed
     if (!this.abortController.signal.aborted) {
       this.abortController.abort();
@@ -666,17 +593,24 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
   process = debounce(this.processImmediate, this.config.PROCESS_DEBOUNCE_MS);
 
   /**
-   * Applies the segmented image to the entity, replacing the entity's objects with the masked image.
+   * Applies the segmented image to the entity.
    */
   apply = () => {
-    const imageState = this.$imageState.get();
+    if (!this.$hasProcessed.get()) {
+      this.log.error('Cannot apply unprocessed points');
+      return;
+    }
+    const imageState = this.imageState;
     if (!imageState) {
       this.log.error('No image state to apply');
       return;
     }
     this.log.trace('Applying');
 
-    // Rasterize the entity, replacing the objects with the masked image
+    // Commit the buffer, which will move the buffer to from the layers' buffer renderer to its main renderer
+    this.parent.bufferRenderer.commitBuffer();
+
+    // Rasterize the entity, this time replacing the objects with the masked image
     const rect = this.parent.transformer.getRelativeRect();
     this.manager.stateApi.rasterizeEntity({
       entityIdentifier: this.parent.entityIdentifier,
@@ -693,59 +627,6 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
     this.teardown();
   };
 
-  /**
-   * Saves the segmented image as a new entity of the given type.
-   * @param type The type of entity to save the segmented image as.
-   */
-  saveAs = (type: Exclude<CanvasEntityType, 'reference_image'>) => {
-    const imageState = this.$imageState.get();
-    if (!imageState) {
-      this.log.error('No image state to save as');
-      return;
-    }
-    this.log.trace(`Saving as ${type}`);
-
-    // Have the parent adopt the image module - this prevents a flash of the original layer content before the
-    // segmented image is rendered
-    if (this.imageModule) {
-      this.parent.renderer.adoptObjectRenderer(this.imageModule);
-    }
-
-    // Create the new entity with the masked image as its only object
-    const rect = this.parent.transformer.getRelativeRect();
-    const arg = {
-      overrides: {
-        objects: [imageState],
-        position: {
-          x: Math.round(rect.x),
-          y: Math.round(rect.y),
-        },
-      },
-      isSelected: true,
-    };
-
-    switch (type) {
-      case 'raster_layer':
-        this.manager.stateApi.addRasterLayer(arg);
-        break;
-      case 'control_layer':
-        this.manager.stateApi.addControlLayer(arg);
-        break;
-      case 'inpaint_mask':
-        this.manager.stateApi.addInpaintMask(arg);
-        break;
-      case 'regional_guidance':
-        this.manager.stateApi.addRegionalGuidance(arg);
-        break;
-      default:
-        assert<Equals<typeof type, never>>(false);
-    }
-
-    // Final cleanup and teardown, returning user to main canvas UI
-    this.resetEphemeralState();
-    this.teardown();
-  };
-
   /**
    * Resets the module (e.g. remove all points and the mask image).
    *
@@ -802,39 +683,30 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
     for (const point of this.$points.get()) {
       point.konva.circle.destroy();
     }
-
-    // If the image module exists, and is a child of the group, destroy it. It might not be a child of the group if
-    // the user has applied the segmented image and the image has been adopted by the parent entity.
-    if (this.imageModule && this.imageModule.konva.group.parent === this.konva.group) {
-      this.imageModule.destroy();
-      this.imageModule = null;
-    }
-    if (this.konva.maskTween) {
-      this.konva.maskTween.destroy();
-      this.konva.maskTween = null;
+    if (this.maskedImage) {
+      this.maskedImage.destroy();
     }
 
     // Empty internal module state
     this.$points.set([]);
-    this.$imageState.set(null);
+    this.imageState = null;
     this.$pointType.set(1);
-    this.$invert.set(false);
-    this.$lastProcessedHash.set('');
+    this.$hasProcessed.set(false);
     this.$isProcessing.set(false);
 
     // Reset non-ephemeral konva nodes
     this.konva.compositingRect.visible(false);
     this.konva.maskGroup.clearCache();
+
+    // The parent module's buffer should be reset & forcibly sync the cache
+    this.parent.bufferRenderer.clearBuffer();
+    this.parent.renderer.syncKonvaCache(true);
   };
 
   /**
    * Builds a graph for segmenting an image with the given image DTO.
    */
-  static buildGraph = (
-    { image_name }: ImageDTO,
-    points: SAMPointWithId[],
-    invert: boolean
-  ): { graph: Graph; outputNodeId: string } => {
+  buildGraph = ({ image_name }: ImageDTO): { graph: Graph; outputNodeId: string } => {
     const graph = new Graph(getPrefixedId('canvas_segment_anything'));
 
     // TODO(psyche): When SAM2 is available in transformers, use it here
@@ -844,7 +716,7 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
       type: 'segment_anything',
       model: 'segment-anything-huge',
       image: { image_name },
-      point_lists: [{ points: points.map(({ x, y, label }) => ({ x, y, label })) }],
+      point_lists: [{ points: this.getSAMPoints() }],
       mask_filter: 'largest',
     });
 
@@ -853,7 +725,6 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
       id: getPrefixedId('apply_tensor_mask_to_image'),
       type: 'apply_tensor_mask_to_image',
       image: { image_name },
-      invert,
     });
     graph.addEdge(segmentAnything, 'mask', applyMask, 'mask');
 
@@ -888,11 +759,11 @@ export class CanvasSegmentAnythingModule extends CanvasModuleBase {
         label,
         circle: getKonvaNodeDebugAttrs(konva.circle),
       })),
-      imageState: deepClone(this.$imageState.get()),
-      imageModule: this.imageModule?.repr(),
+      imageState: deepClone(this.imageState),
+      maskedImage: this.maskedImage?.repr(),
       config: deepClone(this.config),
       $isSegmenting: this.$isSegmenting.get(),
-      $lastProcessedHash: this.$lastProcessedHash.get(),
+      $hasProcessed: this.$hasProcessed.get(),
       $isProcessing: this.$isProcessing.get(),
       $pointType: this.$pointType.get(),
       $pointTypeString: this.$pointTypeString.get(),

invokeai/frontend/web/src/features/controlLayers/konva/CanvasStagingAreaModule.ts

@@ -51,16 +51,10 @@ export class CanvasStagingAreaModule extends CanvasModuleBase {
     /**
      * Sync the $isStaging flag with the redux state. $isStaging is used by the manager to determine the global busy
      * state of the canvas.
-     *
-     * We also set the $shouldShowStagedImage flag when we enter staging mode, so that the staged images are shown,
-     * even if the user disabled this in the last staging session.
      */
     this.subscriptions.add(
-      this.manager.stateApi.createStoreSubscription(selectIsStaging, (isStaging, oldIsStaging) => {
+      this.manager.stateApi.createStoreSubscription(selectIsStaging, (isStaging) => {
         this.$isStaging.set(isStaging);
-        if (isStaging && !oldIsStaging) {
-          this.$shouldShowStagedImage.set(true);
-        }
       })
     );
   }

invokeai/frontend/web/src/features/controlLayers/konva/CanvasStateApiModule.ts

@@ -17,16 +17,12 @@ import {
 } from 'features/controlLayers/store/canvasSettingsSlice';
 import {
   bboxChangedFromCanvas,
-  controlLayerAdded,
   entityBrushLineAdded,
   entityEraserLineAdded,
   entityMoved,
   entityRasterized,
   entityRectAdded,
   entityReset,
-  inpaintMaskAdded,
-  rasterLayerAdded,
-  rgAdded,
 } from 'features/controlLayers/store/canvasSlice';
 import { selectCanvasStagingAreaSlice } from 'features/controlLayers/store/canvasStagingAreaSlice';
 import {
@@ -55,7 +51,6 @@ import { getImageDTO } from 'services/api/endpoints/images';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { BatchConfig, ImageDTO, S } from 'services/api/types';
 import { QueueError } from 'services/events/errors';
-import type { Param0 } from 'tsafe';
 import { assert } from 'tsafe';
 
 import type { CanvasEntityAdapter } from './CanvasEntity/types';
@@ -165,34 +160,6 @@ export class CanvasStateApiModule extends CanvasModuleBase {
     this.store.dispatch(entityRectAdded(arg));
   };
 
-  /**
-   * Adds a raster layer to the canvas, pushing state to redux.
-   */
-  addRasterLayer = (arg: Param0<typeof rasterLayerAdded>) => {
-    this.store.dispatch(rasterLayerAdded(arg));
-  };
-
-  /**
-   * Adds a control layer to the canvas, pushing state to redux.
-   */
-  addControlLayer = (arg: Param0<typeof controlLayerAdded>) => {
-    this.store.dispatch(controlLayerAdded(arg));
-  };
-
-  /**
-   * Adds an inpaint mask to the canvas, pushing state to redux.
-   */
-  addInpaintMask = (arg: Param0<typeof inpaintMaskAdded>) => {
-    this.store.dispatch(inpaintMaskAdded(arg));
-  };
-
-  /**
-   * Adds regional guidance to the canvas, pushing state to redux.
-   */
-  addRegionalGuidance = (arg: Param0<typeof rgAdded>) => {
-    this.store.dispatch(rgAdded(arg));
-  };
-
   /**
    * Rasterizes an entity, pushing state to redux.
    */
@@ -293,8 +260,6 @@ export class CanvasStateApiModule extends CanvasModuleBase {
       },
     };
 
-    let didSuceed = false;
-
     /**
      * If a timeout is provided, we will cancel the graph if it takes too long - but we need a way to clear the timeout
      * if the graph completes or errors before the timeout.
@@ -346,8 +311,6 @@ export class CanvasStateApiModule extends CanvasModuleBase {
           return;
         }
 
-        didSuceed = true;
-
         // Ok!
         resolve(getImageDTOResult.value);
       };
@@ -438,10 +401,6 @@ export class CanvasStateApiModule extends CanvasModuleBase {
 
       if (timeout) {
         timeoutId = window.setTimeout(() => {
-          if (didSuceed) {
-            // If we already succeeded, we don't need to do anything
-            return;
-          }
           this.log.trace('Graph canceled by timeout');
           clearListeners();
           cancelGraph();
@@ -451,10 +410,6 @@ export class CanvasStateApiModule extends CanvasModuleBase {
 
       if (signal) {
         signal.addEventListener('abort', () => {
-          if (didSuceed) {
-            // If we already succeeded, we don't need to do anything
-            return;
-          }
           this.log.trace('Graph canceled by signal');
           _clearTimeout();
           clearListeners();

invokeai/frontend/web/src/features/controlLayers/konva/CanvasTool/CanvasEraserToolModule.ts

@@ -216,14 +216,12 @@ export class CanvasEraserToolModule extends CanvasModuleBase {
    */
   onStagePointerDown = async (e: KonvaEventObject<PointerEvent>) => {
     const cursorPos = this.parent.$cursorPos.get();
-    const isPrimaryPointerDown = this.parent.$isPrimaryPointerDown.get();
     const selectedEntity = this.manager.stateApi.getSelectedEntityAdapter();
 
-    if (!cursorPos || !selectedEntity || !isPrimaryPointerDown) {
+    if (!cursorPos || !selectedEntity) {
       /**
        * Can't do anything without:
        * - A cursor position: the cursor is not on the stage
-       * - The mouse is down: the user is not drawing
        * - A selected entity: there is no entity to draw on
        */
       return;

invokeai/frontend/web/src/features/controlLayers/konva/CanvasTool/CanvasToolModule.ts

@@ -160,16 +160,11 @@ export class CanvasToolModule extends CanvasModuleBase {
     const stage = this.manager.stage;
     const tool = this.$tool.get();
     const segmentingAdapter = this.manager.stateApi.$segmentingAdapter.get();
-    const transformingAdapter = this.manager.stateApi.$transformingAdapter.get();
 
-    if (this.manager.stage.getIsDragging()) {
-      this.tools.view.syncCursorStyle();
-    } else if (tool === 'view') {
+    if ((this.manager.stage.getIsDragging() || tool === 'view') && !segmentingAdapter) {
       this.tools.view.syncCursorStyle();
     } else if (segmentingAdapter) {
       segmentingAdapter.segmentAnything.syncCursorStyle();
-    } else if (transformingAdapter) {
-      // The transformer handles cursor style via events
     } else if (this.manager.stateApi.$isFiltering.get()) {
       stage.setCursor('not-allowed');
     } else if (this.manager.stagingArea.$isStaging.get()) {

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

@@ -126,13 +126,6 @@ export const floorCoord = (coord: Coordinate): Coordinate => {
   };
 };
 
-export const roundCoord = (coord: Coordinate): Coordinate => {
-  return {
-    x: Math.round(coord.x),
-    y: Math.round(coord.y),
-  };
-};
-
 /**
  * Snaps a position to the edge of the given rect if within a threshold of the edge
  * @param pos The position to snap

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

@@ -29,7 +29,7 @@ import { isMainModelBase, zModelIdentifierField } from 'features/nodes/types/com
 import { ASPECT_RATIO_MAP } from 'features/parameters/components/Bbox/constants';
 import { getGridSize, getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import type { IRect } from 'konva/lib/types';
-import { merge } from 'lodash-es';
+import { merge, omit } from 'lodash-es';
 import type { UndoableOptions } from 'redux-undo';
 import type { ControlNetModelConfig, ImageDTO, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
 import { assert } from 'tsafe';
@@ -57,13 +57,13 @@ import type {
 } from './types';
 import { getEntityIdentifier, isRenderableEntity } from './types';
 import {
-  converters,
   getControlLayerState,
   getInpaintMaskState,
   getRasterLayerState,
   getReferenceImageState,
   getRegionalGuidanceState,
   imageDTOToImageWithDims,
+  initialControlNet,
   initialIPAdapter,
 } from './util';
 
@@ -157,25 +157,28 @@ export const canvasSlice = createSlice({
       reducer: (
         state,
         action: PayloadAction<
-          EntityIdentifierPayload<
-            { newId: string; overrides?: Partial<CanvasControlLayerState>; replace?: boolean },
-            'raster_layer'
-          >
+          EntityIdentifierPayload<{ newId: string; overrides?: Partial<CanvasControlLayerState> }, 'raster_layer'>
         >
       ) => {
-        const { entityIdentifier, newId, overrides, replace } = action.payload;
+        const { entityIdentifier, newId, overrides } = action.payload;
         const layer = selectEntity(state, entityIdentifier);
         if (!layer) {
           return;
         }
 
         // Convert the raster layer to control layer
-        const controlLayerState = converters.rasterLayer.toControlLayer(newId, layer, overrides);
+        const controlLayerState: CanvasControlLayerState = {
+          ...deepClone(layer),
+          id: newId,
+          type: 'control_layer',
+          controlAdapter: deepClone(initialControlNet),
+          withTransparencyEffect: true,
+        };
 
-        if (replace) {
-          // Remove the raster layer
-          state.rasterLayers.entities = state.rasterLayers.entities.filter((layer) => layer.id !== entityIdentifier.id);
-        }
+        merge(controlLayerState, overrides);
+
+        // Remove the raster layer
+        state.rasterLayers.entities = state.rasterLayers.entities.filter((layer) => layer.id !== entityIdentifier.id);
 
         // Add the converted control layer
         state.controlLayers.entities.push(controlLayerState);
@@ -183,90 +186,11 @@ export const canvasSlice = createSlice({
         state.selectedEntityIdentifier = { type: controlLayerState.type, id: controlLayerState.id };
       },
       prepare: (
-        payload: EntityIdentifierPayload<
-          { overrides?: Partial<CanvasControlLayerState>; replace?: boolean } | undefined,
-          'raster_layer'
-        >
+        payload: EntityIdentifierPayload<{ overrides?: Partial<CanvasControlLayerState> } | undefined, 'raster_layer'>
       ) => ({
         payload: { ...payload, newId: getPrefixedId('control_layer') },
       }),
     },
-    rasterLayerConvertedToInpaintMask: {
-      reducer: (
-        state,
-        action: PayloadAction<
-          EntityIdentifierPayload<
-            { newId: string; overrides?: Partial<CanvasInpaintMaskState>; replace?: boolean },
-            'raster_layer'
-          >
-        >
-      ) => {
-        const { entityIdentifier, newId, overrides, replace } = action.payload;
-        const layer = selectEntity(state, entityIdentifier);
-        if (!layer) {
-          return;
-        }
-
-        // Convert the raster layer to inpaint mask
-        const inpaintMaskState = converters.rasterLayer.toInpaintMask(newId, layer, overrides);
-
-        if (replace) {
-          // Remove the raster layer
-          state.rasterLayers.entities = state.rasterLayers.entities.filter((layer) => layer.id !== entityIdentifier.id);
-        }
-
-        // Add the converted inpaint mask
-        state.inpaintMasks.entities.push(inpaintMaskState);
-
-        state.selectedEntityIdentifier = { type: inpaintMaskState.type, id: inpaintMaskState.id };
-      },
-      prepare: (
-        payload: EntityIdentifierPayload<
-          { overrides?: Partial<CanvasInpaintMaskState>; replace?: boolean } | undefined,
-          'raster_layer'
-        >
-      ) => ({
-        payload: { ...payload, newId: getPrefixedId('inpaint_mask') },
-      }),
-    },
-    rasterLayerConvertedToRegionalGuidance: {
-      reducer: (
-        state,
-        action: PayloadAction<
-          EntityIdentifierPayload<
-            { newId: string; overrides?: Partial<CanvasRegionalGuidanceState>; replace?: boolean },
-            'raster_layer'
-          >
-        >
-      ) => {
-        const { entityIdentifier, newId, overrides, replace } = action.payload;
-        const layer = selectEntity(state, entityIdentifier);
-        if (!layer) {
-          return;
-        }
-
-        // Convert the raster layer to inpaint mask
-        const regionalGuidanceState = converters.rasterLayer.toRegionalGuidance(newId, layer, overrides);
-
-        if (replace) {
-          // Remove the raster layer
-          state.rasterLayers.entities = state.rasterLayers.entities.filter((layer) => layer.id !== entityIdentifier.id);
-        }
-
-        // Add the converted inpaint mask
-        state.regionalGuidance.entities.push(regionalGuidanceState);
-
-        state.selectedEntityIdentifier = { type: regionalGuidanceState.type, id: regionalGuidanceState.id };
-      },
-      prepare: (
-        payload: EntityIdentifierPayload<
-          { overrides?: Partial<CanvasRegionalGuidanceState>; replace?: boolean } | undefined,
-          'raster_layer'
-        >
-      ) => ({
-        payload: { ...payload, newId: getPrefixedId('regional_guidance') },
-      }),
-    },
     //#region Control layers
     controlLayerAdded: {
       reducer: (
@@ -293,125 +217,32 @@ export const canvasSlice = createSlice({
       state.selectedEntityIdentifier = { type: 'control_layer', id: data.id };
     },
     controlLayerConvertedToRasterLayer: {
-      reducer: (
-        state,
-        action: PayloadAction<
-          EntityIdentifierPayload<
-            { newId: string; overrides?: Partial<CanvasRasterLayerState>; replace?: boolean },
-            'control_layer'
-          >
-        >
-      ) => {
-        const { entityIdentifier, newId, overrides, replace } = action.payload;
+      reducer: (state, action: PayloadAction<EntityIdentifierPayload<{ newId: string }, 'control_layer'>>) => {
+        const { entityIdentifier, newId } = action.payload;
         const layer = selectEntity(state, entityIdentifier);
         if (!layer) {
           return;
         }
 
         // Convert the raster layer to control layer
-        const rasterLayerState = converters.controlLayer.toRasterLayer(newId, layer, overrides);
+        const rasterLayerState: CanvasRasterLayerState = {
+          ...omit(deepClone(layer), ['type', 'controlAdapter', 'withTransparencyEffect']),
+          id: newId,
+          type: 'raster_layer',
+        };
 
-        if (replace) {
-          // Remove the control layer
-          state.controlLayers.entities = state.controlLayers.entities.filter(
-            (layer) => layer.id !== entityIdentifier.id
-          );
-        }
+        // Remove the control layer
+        state.controlLayers.entities = state.controlLayers.entities.filter((layer) => layer.id !== entityIdentifier.id);
 
         // Add the new raster layer
         state.rasterLayers.entities.push(rasterLayerState);
 
         state.selectedEntityIdentifier = { type: rasterLayerState.type, id: rasterLayerState.id };
       },
-      prepare: (
-        payload: EntityIdentifierPayload<
-          { overrides?: Partial<CanvasRasterLayerState>; replace?: boolean } | undefined,
-          'control_layer'
-        >
-      ) => ({
+      prepare: (payload: EntityIdentifierPayload<void, 'control_layer'>) => ({
         payload: { ...payload, newId: getPrefixedId('raster_layer') },
       }),
     },
-    controlLayerConvertedToInpaintMask: {
-      reducer: (
-        state,
-        action: PayloadAction<
-          EntityIdentifierPayload<
-            { newId: string; overrides?: Partial<CanvasInpaintMaskState>; replace?: boolean },
-            'control_layer'
-          >
-        >
-      ) => {
-        const { entityIdentifier, newId, overrides, replace } = action.payload;
-        const layer = selectEntity(state, entityIdentifier);
-        if (!layer) {
-          return;
-        }
-
-        // Convert the control layer to inpaint mask
-        const inpaintMaskState = converters.controlLayer.toInpaintMask(newId, layer, overrides);
-
-        if (replace) {
-          // Remove the control layer
-          state.controlLayers.entities = state.controlLayers.entities.filter(
-            (layer) => layer.id !== entityIdentifier.id
-          );
-        }
-
-        // Add the new inpaint mask
-        state.inpaintMasks.entities.push(inpaintMaskState);
-
-        state.selectedEntityIdentifier = { type: inpaintMaskState.type, id: inpaintMaskState.id };
-      },
-      prepare: (
-        payload: EntityIdentifierPayload<
-          { overrides?: Partial<CanvasInpaintMaskState>; replace?: boolean } | undefined,
-          'control_layer'
-        >
-      ) => ({
-        payload: { ...payload, newId: getPrefixedId('inpaint_mask') },
-      }),
-    },
-    controlLayerConvertedToRegionalGuidance: {
-      reducer: (
-        state,
-        action: PayloadAction<
-          EntityIdentifierPayload<
-            { newId: string; overrides?: Partial<CanvasRegionalGuidanceState>; replace?: boolean },
-            'control_layer'
-          >
-        >
-      ) => {
-        const { entityIdentifier, newId, overrides, replace } = action.payload;
-        const layer = selectEntity(state, entityIdentifier);
-        if (!layer) {
-          return;
-        }
-
-        // Convert the control layer to regional guidance
-        const regionalGuidanceState = converters.controlLayer.toRegionalGuidance(newId, layer, overrides);
-
-        if (replace) {
-          // Remove the control layer
-          state.controlLayers.entities = state.controlLayers.entities.filter(
-            (layer) => layer.id !== entityIdentifier.id
-          );
-        }
-
-        // Add the new regional guidance
-        state.regionalGuidance.entities.push(regionalGuidanceState);
-
-        state.selectedEntityIdentifier = { type: regionalGuidanceState.type, id: regionalGuidanceState.id };
-      },
-      prepare: (
-        payload: EntityIdentifierPayload<
-          { overrides?: Partial<CanvasRegionalGuidanceState>; replace?: boolean } | undefined,
-          'control_layer'
-        >
-      ) => ({
-        payload: { ...payload, newId: getPrefixedId('regional_guidance') },
-      }),
-    },
     controlLayerModelChanged: (
       state,
       action: PayloadAction<
@@ -616,46 +447,6 @@ export const canvasSlice = createSlice({
       state.regionalGuidance.entities.push(data);
       state.selectedEntityIdentifier = { type: 'regional_guidance', id: data.id };
     },
-    rgConvertedToInpaintMask: {
-      reducer: (
-        state,
-        action: PayloadAction<
-          EntityIdentifierPayload<
-            { newId: string; overrides?: Partial<CanvasInpaintMaskState>; replace?: boolean },
-            'regional_guidance'
-          >
-        >
-      ) => {
-        const { entityIdentifier, newId, overrides, replace } = action.payload;
-        const layer = selectEntity(state, entityIdentifier);
-        if (!layer) {
-          return;
-        }
-
-        // Convert the regional guidance to inpaint mask
-        const inpaintMaskState = converters.regionalGuidance.toInpaintMask(newId, layer, overrides);
-
-        if (replace) {
-          // Remove the regional guidance
-          state.regionalGuidance.entities = state.regionalGuidance.entities.filter(
-            (layer) => layer.id !== entityIdentifier.id
-          );
-        }
-
-        // Add the new inpaint mask
-        state.inpaintMasks.entities.push(inpaintMaskState);
-
-        state.selectedEntityIdentifier = { type: inpaintMaskState.type, id: inpaintMaskState.id };
-      },
-      prepare: (
-        payload: EntityIdentifierPayload<
-          { overrides?: Partial<CanvasInpaintMaskState>; replace?: boolean } | undefined,
-          'regional_guidance'
-        >
-      ) => ({
-        payload: { ...payload, newId: getPrefixedId('inpaint_mask') },
-      }),
-    },
     rgPositivePromptChanged: (
       state,
       action: PayloadAction<EntityIdentifierPayload<{ prompt: string | null }, 'regional_guidance'>>
@@ -853,44 +644,6 @@ export const canvasSlice = createSlice({
       state.inpaintMasks.entities = [data];
       state.selectedEntityIdentifier = { type: 'inpaint_mask', id: data.id };
     },
-    inpaintMaskConvertedToRegionalGuidance: {
-      reducer: (
-        state,
-        action: PayloadAction<
-          EntityIdentifierPayload<
-            { newId: string; overrides?: Partial<CanvasRegionalGuidanceState>; replace?: boolean },
-            'inpaint_mask'
-          >
-        >
-      ) => {
-        const { entityIdentifier, newId, overrides, replace } = action.payload;
-        const layer = selectEntity(state, entityIdentifier);
-        if (!layer) {
-          return;
-        }
-
-        // Convert the inpaint mask to regional guidance
-        const regionalGuidanceState = converters.inpaintMask.toRegionalGuidance(newId, layer, overrides);
-
-        if (replace) {
-          // Remove the inpaint mask
-          state.inpaintMasks.entities = state.inpaintMasks.entities.filter((layer) => layer.id !== entityIdentifier.id);
-        }
-
-        // Add the new regional guidance
-        state.regionalGuidance.entities.push(regionalGuidanceState);
-
-        state.selectedEntityIdentifier = { type: regionalGuidanceState.type, id: regionalGuidanceState.id };
-      },
-      prepare: (
-        payload: EntityIdentifierPayload<
-          { overrides?: Partial<CanvasRegionalGuidanceState>; replace?: boolean } | undefined,
-          'inpaint_mask'
-        >
-      ) => ({
-        payload: { ...payload, newId: getPrefixedId('regional_guidance') },
-      }),
-    },
     //#region BBox
     bboxScaledWidthChanged: (state, action: PayloadAction<number>) => {
       const gridSize = getGridSize(state.bbox.modelBase);
@@ -1457,14 +1210,10 @@ export const {
   rasterLayerAdded,
   // rasterLayerRecalled,
   rasterLayerConvertedToControlLayer,
-  rasterLayerConvertedToInpaintMask,
-  rasterLayerConvertedToRegionalGuidance,
   // Control layers
   controlLayerAdded,
   // controlLayerRecalled,
   controlLayerConvertedToRasterLayer,
-  controlLayerConvertedToInpaintMask,
-  controlLayerConvertedToRegionalGuidance,
   controlLayerModelChanged,
   controlLayerControlModeChanged,
   controlLayerWeightChanged,
@@ -1482,7 +1231,6 @@ export const {
   // Regions
   rgAdded,
   // rgRecalled,
-  rgConvertedToInpaintMask,
   rgPositivePromptChanged,
   rgNegativePromptChanged,
   rgAutoNegativeToggled,
@@ -1496,7 +1244,6 @@ export const {
   rgIPAdapterCLIPVisionModelChanged,
   // Inpaint mask
   inpaintMaskAdded,
-  inpaintMaskConvertedToRegionalGuidance,
   // inpaintMaskRecalled,
 } = canvasSlice.actions;
 

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

@@ -349,27 +349,6 @@ export const buildSelectIsSelected = (entityIdentifier: CanvasEntityIdentifier)
   );
 };
 
-/**
- * Builds a selector that selects if the entity is empty.
- *
- * Reference images are considered empty if the IP adapter is empty.
- *
- * Other entities are considered empty if they have no objects.
- */
-export const buildSelectHasObjects = (entityIdentifier: CanvasEntityIdentifier) => {
-  return createSelector(selectCanvasSlice, (canvas) => {
-    const entity = selectEntity(canvas, entityIdentifier);
-
-    if (!entity) {
-      return false;
-    }
-    if (entity.type === 'reference_image') {
-      return entity.ipAdapter.image !== null;
-    }
-    return entity.objects.length > 0;
-  });
-};
-
 export const selectWidth = createSelector(selectCanvasSlice, (canvas) => canvas.bbox.rect.width);
 export const selectHeight = createSelector(selectCanvasSlice, (canvas) => canvas.bbox.rect.height);
 export const selectAspectRatioID = createSelector(selectCanvasSlice, (canvas) => canvas.bbox.aspectRatio.id);

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

@@ -131,8 +131,7 @@ const zSAMPoint = z.object({
   y: z.number().int().gte(0),
   label: zSAMPointLabel,
 });
-type SAMPoint = z.infer<typeof zSAMPoint>;
-export type SAMPointWithId = SAMPoint & { id: string };
+export type SAMPoint = z.infer<typeof zSAMPoint>;
 
 const zRect = z.object({
   x: z.number(),

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

@@ -184,153 +184,3 @@ export const getInpaintMaskState = (
   merge(entityState, overrides);
   return entityState;
 };
-
-const convertRasterLayerToControlLayer = (
-  newId: string,
-  rasterLayerState: CanvasRasterLayerState,
-  overrides?: Partial<CanvasControlLayerState>
-): CanvasControlLayerState => {
-  const { name, objects, position } = rasterLayerState;
-  const controlLayerState = getControlLayerState(newId, {
-    name,
-    objects,
-    position,
-  });
-  merge(controlLayerState, overrides);
-  return controlLayerState;
-};
-
-const convertRasterLayerToInpaintMask = (
-  newId: string,
-  rasterLayerState: CanvasRasterLayerState,
-  overrides?: Partial<CanvasInpaintMaskState>
-): CanvasInpaintMaskState => {
-  const { name, objects, position } = rasterLayerState;
-  const inpaintMaskState = getInpaintMaskState(newId, {
-    name,
-    objects,
-    position,
-  });
-  merge(inpaintMaskState, overrides);
-  return inpaintMaskState;
-};
-
-const convertRasterLayerToRegionalGuidance = (
-  newId: string,
-  rasterLayerState: CanvasRasterLayerState,
-  overrides?: Partial<CanvasRegionalGuidanceState>
-): CanvasRegionalGuidanceState => {
-  const { name, objects, position } = rasterLayerState;
-  const regionalGuidanceState = getRegionalGuidanceState(newId, {
-    name,
-    objects,
-    position,
-  });
-  merge(regionalGuidanceState, overrides);
-  return regionalGuidanceState;
-};
-
-const convertControlLayerToRasterLayer = (
-  newId: string,
-  controlLayerState: CanvasControlLayerState,
-  overrides?: Partial<CanvasRasterLayerState>
-): CanvasRasterLayerState => {
-  const { name, objects, position } = controlLayerState;
-  const rasterLayerState = getRasterLayerState(newId, {
-    name,
-    objects,
-    position,
-  });
-  merge(rasterLayerState, overrides);
-  return rasterLayerState;
-};
-
-const convertControlLayerToInpaintMask = (
-  newId: string,
-  rasterLayerState: CanvasControlLayerState,
-  overrides?: Partial<CanvasInpaintMaskState>
-): CanvasInpaintMaskState => {
-  const { name, objects, position } = rasterLayerState;
-  const inpaintMaskState = getInpaintMaskState(newId, {
-    name,
-    objects,
-    position,
-  });
-  merge(inpaintMaskState, overrides);
-  return inpaintMaskState;
-};
-
-const convertControlLayerToRegionalGuidance = (
-  newId: string,
-  rasterLayerState: CanvasControlLayerState,
-  overrides?: Partial<CanvasRegionalGuidanceState>
-): CanvasRegionalGuidanceState => {
-  const { name, objects, position } = rasterLayerState;
-  const regionalGuidanceState = getRegionalGuidanceState(newId, {
-    name,
-    objects,
-    position,
-  });
-  merge(regionalGuidanceState, overrides);
-  return regionalGuidanceState;
-};
-
-const convertInpaintMaskToRegionalGuidance = (
-  newId: string,
-  inpaintMaskState: CanvasInpaintMaskState,
-  overrides?: Partial<CanvasRegionalGuidanceState>
-): CanvasRegionalGuidanceState => {
-  const { name, objects, position } = inpaintMaskState;
-  const regionalGuidanceState = getRegionalGuidanceState(newId, {
-    name,
-    objects,
-    position,
-  });
-  merge(regionalGuidanceState, overrides);
-  return regionalGuidanceState;
-};
-
-const convertRegionalGuidanceToInpaintMask = (
-  newId: string,
-  regionalGuidanceState: CanvasRegionalGuidanceState,
-  overrides?: Partial<CanvasInpaintMaskState>
-): CanvasInpaintMaskState => {
-  const { name, objects, position } = regionalGuidanceState;
-  const inpaintMaskState = getInpaintMaskState(newId, {
-    name,
-    objects,
-    position,
-  });
-  merge(inpaintMaskState, overrides);
-  return inpaintMaskState;
-};
-
-/**
- * Supported conversions:
- * - Raster Layer -> Control Layer
- * - Raster Layer -> Inpaint Mask
- * - Raster Layer -> Regional Guidance
- * - Control Layer -> Control Layer
- * - Control Layer -> Inpaint Mask
- * - Control Layer -> Regional Guidance
- * - Inpaint Mask -> Regional Guidance
- * - Regional Guidance -> Inpaint Mask
- */
-export const converters = {
-  rasterLayer: {
-    toControlLayer: convertRasterLayerToControlLayer,
-    toInpaintMask: convertRasterLayerToInpaintMask,
-    toRegionalGuidance: convertRasterLayerToRegionalGuidance,
-  },
-  controlLayer: {
-    toRasterLayer: convertControlLayerToRasterLayer,
-    toInpaintMask: convertControlLayerToInpaintMask,
-    toRegionalGuidance: convertControlLayerToRegionalGuidance,
-  },
-  inpaintMask: {
-    toRegionalGuidance: convertInpaintMaskToRegionalGuidance,
-  },
-  regionalGuidance: {
-    toInpaintMask: convertRegionalGuidanceToInpaintMask,
-  },
-};

invokeai/frontend/web/src/features/dnd/types/index.ts

@@ -42,14 +42,6 @@ export type AddControlLayerFromImageDropData = BaseDropData & {
   actionType: 'ADD_CONTROL_LAYER_FROM_IMAGE';
 };
 
-type AddInpaintMaskFromImageDropData = BaseDropData & {
-  actionType: 'ADD_INPAINT_MASK_FROM_IMAGE';
-};
-
-type AddRegionalGuidanceFromImageDropData = BaseDropData & {
-  actionType: 'ADD_REGIONAL_GUIDANCE_FROM_IMAGE';
-};
-
 export type AddRegionalReferenceImageFromImageDropData = BaseDropData & {
   actionType: 'ADD_REGIONAL_REFERENCE_IMAGE_FROM_IMAGE';
 };
@@ -61,7 +53,7 @@ export type AddGlobalReferenceImageFromImageDropData = BaseDropData & {
 export type ReplaceLayerImageDropData = BaseDropData & {
   actionType: 'REPLACE_LAYER_WITH_IMAGE';
   context: {
-    entityIdentifier: CanvasEntityIdentifier<'control_layer' | 'raster_layer' | 'inpaint_mask' | 'regional_guidance'>;
+    entityIdentifier: CanvasEntityIdentifier<'control_layer' | 'raster_layer'>;
   };
 };
 
@@ -106,9 +98,7 @@ export type TypesafeDroppableData =
   | AddControlLayerFromImageDropData
   | ReplaceLayerImageDropData
   | AddRegionalReferenceImageFromImageDropData
-  | AddGlobalReferenceImageFromImageDropData
-  | AddInpaintMaskFromImageDropData
-  | AddRegionalGuidanceFromImageDropData;
+  | AddGlobalReferenceImageFromImageDropData;
 
 type BaseDragData = {
   id: string;

invokeai/frontend/web/src/features/dnd/util/isValidDrop.ts

@@ -17,8 +17,6 @@ export const isValidDrop = (overData?: TypesafeDroppableData | null, activeData?
     case 'SET_RG_IP_ADAPTER_IMAGE':
     case 'ADD_RASTER_LAYER_FROM_IMAGE':
     case 'ADD_CONTROL_LAYER_FROM_IMAGE':
-    case 'ADD_INPAINT_MASK_FROM_IMAGE':
-    case 'ADD_REGIONAL_GUIDANCE_FROM_IMAGE':
     case 'SET_UPSCALE_INITIAL_IMAGE':
     case 'SET_NODES_IMAGE':
     case 'SELECT_FOR_COMPARE':

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

@@ -1,4 +1,4 @@
-import { Link } from '@invoke-ai/ui-library';
+import { Flex, Link, Spacer, Text } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { $projectName, $projectUrl } from 'app/store/nanostores/projectId';
 import { memo } from 'react';
@@ -9,13 +9,15 @@ export const GalleryHeader = memo(() => {
 
   if (projectName && projectUrl) {
     return (
-      <Link fontSize="md" fontWeight="semibold" noOfLines={1} wordBreak="break-all" href={projectUrl}>
-        {projectName}
-      </Link>
+      <Flex gap={2} alignItems="center" justifyContent="space-evenly" pe={2} w="50%">
+        <Text fontSize="md" fontWeight="semibold" noOfLines={1} wordBreak="break-all" w="full" textAlign="center">
+          <Link href={projectUrl}>{projectName}</Link>
+        </Text>
+      </Flex>
     );
   }
 
-  return null;
+  return <Spacer />;
 });
 
 GalleryHeader.displayName = 'GalleryHeader';

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

@@ -51,8 +51,8 @@ const GalleryPanelContent = () => {
 
   return (
     <Flex ref={galleryPanelFocusRef} position="relative" flexDirection="column" h="full" w="full" tabIndex={-1}>
-      <Flex alignItems="center" justifyContent="space-between" w="full">
-        <Flex flexGrow={1} flexBasis={0}>
+      <Flex alignItems="center" w="full">
+        <Flex w="25%">
           <Button
             size="sm"
             variant="ghost"
@@ -62,10 +62,8 @@ const GalleryPanelContent = () => {
             {boardsListPanel.isCollapsed ? t('boards.viewBoards') : t('boards.hideBoards')}
           </Button>
         </Flex>
-        <Flex>
-          <GalleryHeader />
-        </Flex>
-        <Flex flexGrow={1} flexBasis={0} justifyContent="flex-end">
+        <GalleryHeader />
+        <Flex h="full" w="25%" justifyContent="flex-end">
           <BoardsSettingsPopover />
           <IconButton
             size="sm"

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

@@ -1,11 +1,9 @@
-import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
+import { MenuItem } from '@invoke-ai/ui-library';
 import { useImageDTOContext } from 'features/gallery/contexts/ImageDTOContext';
 import { useImageActions } from 'features/gallery/hooks/useImageActions';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import {
-  PiArrowBendUpLeftBold,
   PiArrowsCounterClockwiseBold,
   PiAsteriskBold,
   PiPaintBrushBold,
@@ -16,36 +14,28 @@ import {
 export const ImageMenuItemMetadataRecallActions = memo(() => {
   const { t } = useTranslation();
   const imageDTO = useImageDTOContext();
-  const subMenu = useSubMenu();
 
   const { recallAll, remix, recallSeed, recallPrompts, hasMetadata, hasSeed, hasPrompts, createAsPreset } =
     useImageActions(imageDTO);
 
   return (
-    <MenuItem {...subMenu.parentMenuItemProps} icon={<PiArrowBendUpLeftBold />}>
-      <Menu {...subMenu.menuProps}>
-        <MenuButton {...subMenu.menuButtonProps}>
-          <SubMenuButtonContent label="Recall Metadata" />
-        </MenuButton>
-        <MenuList {...subMenu.menuListProps}>
-          <MenuItem icon={<PiArrowsCounterClockwiseBold />} onClick={remix} isDisabled={!hasMetadata}>
-            {t('parameters.remixImage')}
-          </MenuItem>
-          <MenuItem icon={<PiQuotesBold />} onClick={recallPrompts} isDisabled={!hasPrompts}>
-            {t('parameters.usePrompt')}
-          </MenuItem>
-          <MenuItem icon={<PiPlantBold />} onClick={recallSeed} isDisabled={!hasSeed}>
-            {t('parameters.useSeed')}
-          </MenuItem>
-          <MenuItem icon={<PiAsteriskBold />} onClick={recallAll} isDisabled={!hasMetadata}>
-            {t('parameters.useAll')}
-          </MenuItem>
-          <MenuItem icon={<PiPaintBrushBold />} onClick={createAsPreset} isDisabled={!hasPrompts}>
-            {t('stylePresets.useForTemplate')}
-          </MenuItem>
-        </MenuList>
-      </Menu>
-    </MenuItem>
+    <>
+      <MenuItem icon={<PiArrowsCounterClockwiseBold />} onClickCapture={remix} isDisabled={!hasMetadata}>
+        {t('parameters.remixImage')}
+      </MenuItem>
+      <MenuItem icon={<PiQuotesBold />} onClickCapture={recallPrompts} isDisabled={!hasPrompts}>
+        {t('parameters.usePrompt')}
+      </MenuItem>
+      <MenuItem icon={<PiPlantBold />} onClickCapture={recallSeed} isDisabled={!hasSeed}>
+        {t('parameters.useSeed')}
+      </MenuItem>
+      <MenuItem icon={<PiAsteriskBold />} onClickCapture={recallAll} isDisabled={!hasMetadata}>
+        {t('parameters.useAll')}
+      </MenuItem>
+      <MenuItem icon={<PiPaintBrushBold />} onClickCapture={createAsPreset} isDisabled={!hasPrompts}>
+        {t('stylePresets.useForTemplate')}
+      </MenuItem>
+    </>
   );
 });