Update invokeai_version.py

- Split logic to create layers/objects from the updating logic
- Organize and comment functions

invokeai/app/api_app.py

@@ -28,7 +28,7 @@ from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.services.config.config_default import get_config
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
-from invokeai.backend.util.devices import get_torch_device_name
+from invokeai.backend.util.devices import TorchDevice
 
 from ..backend.util.logging import InvokeAILogger
 from .api.dependencies import ApiDependencies
@@ -63,7 +63,7 @@ logger = InvokeAILogger.get_logger(config=app_config)
 mimetypes.add_type("application/javascript", ".js")
 mimetypes.add_type("text/css", ".css")
 
-torch_device_name = get_torch_device_name()
+torch_device_name = TorchDevice.get_torch_device_name()
 logger.info(f"Using torch device: {torch_device_name}")
 
 

invokeai/app/invocations/compel.py

@@ -24,7 +24,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     ConditioningFieldData,
     SDXLConditioningInfo,
 )
-from invokeai.backend.util.devices import torch_dtype
+from invokeai.backend.util.devices import TorchDevice
 
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 from .model import CLIPField
@@ -99,7 +99,7 @@ class CompelInvocation(BaseInvocation):
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
                 textual_inversion_manager=ti_manager,
-                dtype_for_device_getter=torch_dtype,
+                dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,
             )
 
@@ -193,7 +193,7 @@ class SDXLPromptInvocationBase:
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
                 textual_inversion_manager=ti_manager,
-                dtype_for_device_getter=torch_dtype,
+                dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,  # TODO:
                 returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,  # TODO: clip skip
                 requires_pooled=get_pooled,

invokeai/app/invocations/ip_adapter.py

@@ -4,20 +4,8 @@ from typing import List, Literal, Optional, Union
 from pydantic import BaseModel, Field, field_validator, model_validator
 from typing_extensions import Self
 
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    Input,
-    InputField,
-    OutputField,
-    TensorField,
-    UIType,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, TensorField, UIType
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
@@ -36,6 +24,7 @@ class IPAdapterField(BaseModel):
     ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model to use.")
     image_encoder_model: ModelIdentifierField = Field(description="The name of the CLIP image encoder model.")
     weight: Union[float, List[float]] = Field(default=1, description="The weight given to the IP-Adapter.")
+    target_blocks: List[str] = Field(default=[], description="The IP Adapter blocks to apply")
     begin_step_percent: float = Field(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
     )
@@ -69,7 +58,7 @@ class IPAdapterOutput(BaseInvocationOutput):
 CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.3.0")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.0")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -90,6 +79,9 @@ class IPAdapterInvocation(BaseInvocation):
     weight: Union[float, List[float]] = InputField(
         default=1, description="The weight given to the IP-Adapter", title="Weight"
     )
+    method: Literal["full", "style", "composition"] = InputField(
+        default="full", description="The method to apply the IP-Adapter"
+    )
     begin_step_percent: float = InputField(
         default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
     )
@@ -124,12 +116,32 @@ class IPAdapterInvocation(BaseInvocation):
 
         image_encoder_model = self._get_image_encoder(context, image_encoder_model_name)
 
+        if self.method == "style":
+            if ip_adapter_info.base == "sd-1":
+                target_blocks = ["up_blocks.1"]
+            elif ip_adapter_info.base == "sdxl":
+                target_blocks = ["up_blocks.0.attentions.1"]
+            else:
+                raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
+        elif self.method == "composition":
+            if ip_adapter_info.base == "sd-1":
+                target_blocks = ["down_blocks.2", "mid_block"]
+            elif ip_adapter_info.base == "sdxl":
+                target_blocks = ["down_blocks.2.attentions.1"]
+            else:
+                raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
+        elif self.method == "full":
+            target_blocks = ["block"]
+        else:
+            raise ValueError(f"Unexpected IP-Adapter method: '{self.method}'.")
+
         return IPAdapterOutput(
             ip_adapter=IPAdapterField(
                 image=self.image,
                 ip_adapter_model=self.ip_adapter_model,
                 image_encoder_model=ModelIdentifierField.from_config(image_encoder_model),
                 weight=self.weight,
+                target_blocks=target_blocks,
                 begin_step_percent=self.begin_step_percent,
                 end_step_percent=self.end_step_percent,
                 mask=self.mask,

invokeai/app/invocations/latent.py

@@ -51,6 +51,7 @@ from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, LoadedModel
+from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
@@ -72,15 +73,12 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
     image_resized_to_grid_as_tensor,
 )
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.util.devices import choose_precision, choose_torch_device
+from ...backend.util.devices import TorchDevice
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 from .controlnet_image_processors import ControlField
 from .model import ModelIdentifierField, UNetField, VAEField
 
-if choose_torch_device() == torch.device("mps"):
-    from torch import mps
-
-DEFAULT_PRECISION = choose_precision(choose_torch_device())
+DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()
 
 
 @invocation_output("scheduler_output")
@@ -188,7 +186,7 @@ class GradientMaskOutput(BaseInvocationOutput):
     title="Create Gradient Mask",
     tags=["mask", "denoise"],
     category="latents",
-    version="1.0.0",
+    version="1.1.0",
 )
 class CreateGradientMaskInvocation(BaseInvocation):
     """Creates mask for denoising model run."""
@@ -201,6 +199,32 @@ class CreateGradientMaskInvocation(BaseInvocation):
     minimum_denoise: float = InputField(
         default=0.0, ge=0, le=1, description="Minimum denoise level for the coherence region", ui_order=4
     )
+    image: Optional[ImageField] = InputField(
+        default=None,
+        description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
+        title="[OPTIONAL] Image",
+        ui_order=6,
+    )
+    unet: Optional[UNetField] = InputField(
+        description="OPTIONAL: If the Unet is a specialized Inpainting model, masked_latents will be generated from the image with the VAE",
+        default=None,
+        input=Input.Connection,
+        title="[OPTIONAL] UNet",
+        ui_order=5,
+    )
+    vae: Optional[VAEField] = InputField(
+        default=None,
+        description="OPTIONAL: Only connect for specialized Inpainting models, masked_latents will be generated from the image with the VAE",
+        title="[OPTIONAL] VAE",
+        input=Input.Connection,
+        ui_order=7,
+    )
+    tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=8)
+    fp32: bool = InputField(
+        default=DEFAULT_PRECISION == "float32",
+        description=FieldDescriptions.fp32,
+        ui_order=9,
+    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> GradientMaskOutput:
@@ -236,8 +260,27 @@ class CreateGradientMaskInvocation(BaseInvocation):
         expanded_mask_image = Image.fromarray((expanded_mask.squeeze(0).numpy() * 255).astype(np.uint8), mode="L")
         expanded_image_dto = context.images.save(expanded_mask_image)
 
+        masked_latents_name = None
+        if self.unet is not None and self.vae is not None and self.image is not None:
+            # all three fields must be present at the same time
+            main_model_config = context.models.get_config(self.unet.unet.key)
+            assert isinstance(main_model_config, MainConfigBase)
+            if main_model_config.variant is ModelVariantType.Inpaint:
+                mask = blur_tensor
+                vae_info: LoadedModel = context.models.load(self.vae.vae)
+                image = context.images.get_pil(self.image.image_name)
+                image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+                if image_tensor.dim() == 3:
+                    image_tensor = image_tensor.unsqueeze(0)
+                img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+                masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
+                masked_latents = ImageToLatentsInvocation.vae_encode(
+                    vae_info, self.fp32, self.tiled, masked_image.clone()
+                )
+                masked_latents_name = context.tensors.save(tensor=masked_latents)
+
         return GradientMaskOutput(
-            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=None, gradient=True),
+            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=masked_latents_name, gradient=True),
             expanded_mask_area=ImageField(image_name=expanded_image_dto.image_name),
         )
 
@@ -682,6 +725,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 IPAdapterData(
                     ip_adapter_model=ip_adapter_model,
                     weight=single_ip_adapter.weight,
+                    target_blocks=single_ip_adapter.target_blocks,
                     begin_step_percent=single_ip_adapter.begin_step_percent,
                     end_step_percent=single_ip_adapter.end_step_percent,
                     ip_adapter_conditioning=IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds),
@@ -959,9 +1003,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
             result_latents = result_latents.to("cpu")
-            torch.cuda.empty_cache()
-            if choose_torch_device() == torch.device("mps"):
-                mps.empty_cache()
+            TorchDevice.empty_cache()
 
             name = context.tensors.save(tensor=result_latents)
         return LatentsOutput.build(latents_name=name, latents=result_latents, seed=None)
@@ -1028,9 +1070,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 vae.disable_tiling()
 
             # clear memory as vae decode can request a lot
-            torch.cuda.empty_cache()
-            if choose_torch_device() == torch.device("mps"):
-                mps.empty_cache()
+            TorchDevice.empty_cache()
 
             with torch.inference_mode():
                 # copied from diffusers pipeline
@@ -1042,9 +1082,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
                 image = VaeImageProcessor.numpy_to_pil(np_image)[0]
 
-        torch.cuda.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
+        TorchDevice.empty_cache()
 
         image_dto = context.images.save(image=image)
 
@@ -1083,9 +1121,7 @@ class ResizeLatentsInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.tensors.load(self.latents.latents_name)
-
-        # TODO:
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
 
         resized_latents = torch.nn.functional.interpolate(
             latents.to(device),
@@ -1096,9 +1132,8 @@ class ResizeLatentsInvocation(BaseInvocation):
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
-        torch.cuda.empty_cache()
-        if device == torch.device("mps"):
-            mps.empty_cache()
+
+        TorchDevice.empty_cache()
 
         name = context.tensors.save(tensor=resized_latents)
         return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@@ -1125,8 +1160,7 @@ class ScaleLatentsInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
-        # TODO:
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
 
         # resizing
         resized_latents = torch.nn.functional.interpolate(
@@ -1138,9 +1172,7 @@ class ScaleLatentsInvocation(BaseInvocation):
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
-        torch.cuda.empty_cache()
-        if device == torch.device("mps"):
-            mps.empty_cache()
+        TorchDevice.empty_cache()
 
         name = context.tensors.save(tensor=resized_latents)
         return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@@ -1272,8 +1304,7 @@ class BlendLatentsInvocation(BaseInvocation):
         if latents_a.shape != latents_b.shape:
             raise Exception("Latents to blend must be the same size.")
 
-        # TODO:
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
 
         def slerp(
             t: Union[float, npt.NDArray[Any]],  # FIXME: maybe use np.float32 here?
@@ -1326,9 +1357,8 @@ class BlendLatentsInvocation(BaseInvocation):
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         blended_latents = blended_latents.to("cpu")
-        torch.cuda.empty_cache()
-        if device == torch.device("mps"):
-            mps.empty_cache()
+
+        TorchDevice.empty_cache()
 
         name = context.tensors.save(tensor=blended_latents)
         return LatentsOutput.build(latents_name=name, latents=blended_latents)

invokeai/app/invocations/mask.py

@@ -1,7 +1,8 @@
+import numpy as np
 import torch
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext, invocation
-from invokeai.app.invocations.fields import InputField, TensorField, WithMetadata
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, InvocationContext, invocation
+from invokeai.app.invocations.fields import ImageField, InputField, TensorField, WithMetadata
 from invokeai.app.invocations.primitives import MaskOutput
 
 
@@ -34,3 +35,86 @@ class RectangleMaskInvocation(BaseInvocation, WithMetadata):
             width=self.width,
             height=self.height,
         )
+
+
+@invocation(
+    "alpha_mask_to_tensor",
+    title="Alpha Mask to Tensor",
+    tags=["conditioning"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Beta,
+)
+class AlphaMaskToTensorInvocation(BaseInvocation):
+    """Convert a mask image to a tensor. Opaque regions are 1 and transparent regions are 0."""
+
+    image: ImageField = InputField(description="The mask image to convert.")
+    invert: bool = InputField(default=False, description="Whether to invert the mask.")
+
+    def invoke(self, context: InvocationContext) -> MaskOutput:
+        image = context.images.get_pil(self.image.image_name)
+        mask = torch.zeros((1, image.height, image.width), dtype=torch.bool)
+        if self.invert:
+            mask[0] = torch.tensor(np.array(image)[:, :, 3] == 0, dtype=torch.bool)
+        else:
+            mask[0] = torch.tensor(np.array(image)[:, :, 3] > 0, dtype=torch.bool)
+
+        return MaskOutput(
+            mask=TensorField(tensor_name=context.tensors.save(mask)),
+            height=mask.shape[1],
+            width=mask.shape[2],
+        )
+
+
+@invocation(
+    "invert_tensor_mask",
+    title="Invert Tensor Mask",
+    tags=["conditioning"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Beta,
+)
+class InvertTensorMaskInvocation(BaseInvocation):
+    """Inverts a tensor mask."""
+
+    mask: TensorField = InputField(description="The tensor mask to convert.")
+
+    def invoke(self, context: InvocationContext) -> MaskOutput:
+        mask = context.tensors.load(self.mask.tensor_name)
+        inverted = ~mask
+
+        return MaskOutput(
+            mask=TensorField(tensor_name=context.tensors.save(inverted)),
+            height=inverted.shape[1],
+            width=inverted.shape[2],
+        )
+
+
+@invocation(
+    "image_mask_to_tensor",
+    title="Image Mask to Tensor",
+    tags=["conditioning"],
+    category="conditioning",
+    version="1.0.0",
+)
+class ImageMaskToTensorInvocation(BaseInvocation, WithMetadata):
+    """Convert a mask image to a tensor. Converts the image to grayscale and uses thresholding at the specified value."""
+
+    image: ImageField = InputField(description="The mask image to convert.")
+    cutoff: int = InputField(ge=0, le=255, description="Cutoff (<)", default=128)
+    invert: bool = InputField(default=False, description="Whether to invert the mask.")
+
+    def invoke(self, context: InvocationContext) -> MaskOutput:
+        image = context.images.get_pil(self.image.image_name, mode="L")
+
+        mask = torch.zeros((1, image.height, image.width), dtype=torch.bool)
+        if self.invert:
+            mask[0] = torch.tensor(np.array(image)[:, :] >= self.cutoff, dtype=torch.bool)
+        else:
+            mask[0] = torch.tensor(np.array(image)[:, :] < self.cutoff, dtype=torch.bool)
+
+        return MaskOutput(
+            mask=TensorField(tensor_name=context.tensors.save(mask)),
+            height=mask.shape[1],
+            width=mask.shape[2],
+        )

invokeai/app/invocations/metadata.py

@@ -36,6 +36,7 @@ class IPAdapterMetadataField(BaseModel):
     image: ImageField = Field(description="The IP-Adapter image prompt.")
     ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model.")
     clip_vision_model: Literal["ViT-H", "ViT-G"] = Field(description="The CLIP Vision model")
+    method: Literal["full", "style", "composition"] = Field(description="Method to apply IP Weights with")
     weight: Union[float, list[float]] = Field(description="The weight given to the IP-Adapter")
     begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
     end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")

invokeai/app/invocations/noise.py

@@ -9,7 +9,7 @@ from invokeai.app.invocations.fields import FieldDescriptions, InputField, Laten
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.misc import SEED_MAX
 
-from ...backend.util.devices import choose_torch_device, torch_dtype
+from ...backend.util.devices import TorchDevice
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -46,7 +46,7 @@ def get_noise(
             height // downsampling_factor,
             width // downsampling_factor,
         ],
-        dtype=torch_dtype(device),
+        dtype=TorchDevice.choose_torch_dtype(device=device),
         device=noise_device_type,
         generator=generator,
     ).to("cpu")
@@ -111,14 +111,14 @@ class NoiseInvocation(BaseInvocation):
 
     @field_validator("seed", mode="before")
     def modulo_seed(cls, v):
-        """Returns the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
+        """Return the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
         return v % (SEED_MAX + 1)
 
     def invoke(self, context: InvocationContext) -> NoiseOutput:
         noise = get_noise(
             width=self.width,
             height=self.height,
-            device=choose_torch_device(),
+            device=TorchDevice.choose_torch_device(),
             seed=self.seed,
             use_cpu=self.use_cpu,
         )

invokeai/app/invocations/upscale.py

@@ -4,7 +4,6 @@ from typing import Literal
 
 import cv2
 import numpy as np
-import torch
 from PIL import Image
 from pydantic import ConfigDict
 
@@ -14,7 +13,7 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.download_with_progress import download_with_progress_bar
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 from .baseinvocation import BaseInvocation, invocation
 from .fields import InputField, WithBoard, WithMetadata
@@ -35,9 +34,6 @@ ESRGAN_MODEL_URLS: dict[str, str] = {
     "RealESRGAN_x2plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
 }
 
-if choose_torch_device() == torch.device("mps"):
-    from torch import mps
-
 
 @invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.2")
 class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -120,9 +116,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
         upscaled_image = upscaler.upscale(cv2_image)
         pil_image = Image.fromarray(cv2.cvtColor(upscaled_image, cv2.COLOR_BGR2RGB)).convert("RGBA")
 
-        torch.cuda.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
+        TorchDevice.empty_cache()
 
         image_dto = context.images.save(image=pil_image)
 

invokeai/app/services/config/config_default.py

@@ -27,12 +27,12 @@ DEFAULT_RAM_CACHE = 10.0
 DEFAULT_VRAM_CACHE = 0.25
 DEFAULT_CONVERT_CACHE = 20.0
 DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
-PRECISION = Literal["auto", "float16", "bfloat16", "float32", "autocast"]
+PRECISION = Literal["auto", "float16", "bfloat16", "float32"]
 ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
 ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
 LOG_FORMAT = Literal["plain", "color", "syslog", "legacy"]
 LOG_LEVEL = Literal["debug", "info", "warning", "error", "critical"]
-CONFIG_SCHEMA_VERSION = "4.0.0"
+CONFIG_SCHEMA_VERSION = "4.0.1"
 
 
 def get_default_ram_cache_size() -> float:
@@ -105,7 +105,7 @@ class InvokeAIAppConfig(BaseSettings):
         lazy_offload: Keep models in VRAM until their space is needed.
         log_memory_usage: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
         device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
-        precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`, `autocast`
+        precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
         sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
         attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
         attention_slice_size: Slice size, valid when attention_type=="sliced".<br>Valid values: `auto`, `balanced`, `max`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`
@@ -370,6 +370,9 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
             # `max_vram_cache_size` was renamed to `vram` some time in v3, but both names were used
             if k == "max_vram_cache_size" and "vram" not in category_dict:
                 parsed_config_dict["vram"] = v
+            # autocast was removed in v4.0.1
+            if k == "precision" and v == "autocast":
+                parsed_config_dict["precision"] = "auto"
             if k == "conf_path":
                 parsed_config_dict["legacy_models_yaml_path"] = v
             if k == "legacy_conf_dir":
@@ -392,6 +395,28 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
     return config
 
 
+def migrate_v4_0_0_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
+    """Migrate v4.0.0 config dictionary to a current config object.
+
+    Args:
+        config_dict: A dictionary of settings from a v4.0.0 config file.
+
+    Returns:
+        An instance of `InvokeAIAppConfig` with the migrated settings.
+    """
+    parsed_config_dict: dict[str, Any] = {}
+    for k, v in config_dict.items():
+        # autocast was removed from precision in v4.0.1
+        if k == "precision" and v == "autocast":
+            parsed_config_dict["precision"] = "auto"
+        else:
+            parsed_config_dict[k] = v
+        if k == "schema_version":
+            parsed_config_dict[k] = CONFIG_SCHEMA_VERSION
+    config = DefaultInvokeAIAppConfig.model_validate(parsed_config_dict)
+    return config
+
+
 def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
     """Load and migrate a config file to the latest version.
 
@@ -418,17 +443,21 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
             raise RuntimeError(f"Failed to load and migrate v3 config file {config_path}: {e}") from e
         migrated_config.write_file(config_path)
         return migrated_config
-    else:
-        # Attempt to load as a v4 config file
-        try:
-            # Meta is not included in the model fields, so we need to validate it separately
-            config = InvokeAIAppConfig.model_validate(loaded_config_dict)
-            assert (
-                config.schema_version == CONFIG_SCHEMA_VERSION
-            ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
-            return config
-        except Exception as e:
-            raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e
+
+    if loaded_config_dict["schema_version"] == "4.0.0":
+        loaded_config_dict = migrate_v4_0_0_config_dict(loaded_config_dict)
+        loaded_config_dict.write_file(config_path)
+
+    # Attempt to load as a v4 config file
+    try:
+        # Meta is not included in the model fields, so we need to validate it separately
+        config = InvokeAIAppConfig.model_validate(loaded_config_dict)
+        assert (
+            config.schema_version == CONFIG_SCHEMA_VERSION
+        ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+        return config
+    except Exception as e:
+        raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e
 
 
 @lru_cache(maxsize=1)

invokeai/app/services/download/download_default.py

@@ -270,7 +270,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
             job.dest.parent.mkdir(parents=True, exist_ok=True)
             job.download_path = job.dest
 
-        assert job.download_path is not None
+        assert job.download_path
 
         # Don't clobber an existing file. See commit 82c2c85202f88c6d24ff84710f297cfc6ae174af
         # for code that instead resumes an interrupted download.
@@ -280,9 +280,6 @@ class DownloadQueueService(DownloadQueueServiceBase):
         # append ".downloading" to the path
         in_progress_path = self._in_progress_path(job.download_path)
 
-        # catch rare race condition that is appearing in unit tests.
-        assert in_progress_path.parent.exists(), f"Directory doesn't exist! in_progress_path={in_progress_path}; parent={in_progress_path.parent}"
-
         # signal caller that the download is starting. At this point, key fields such as
         # download_path and total_bytes will be populated. We call it here because the might
         # discover that the local file is already complete and generate a COMPLETED status.

invokeai/app/services/model_install/model_install_default.py

@@ -3,7 +3,6 @@
 import locale
 import os
 import re
-import signal
 import threading
 import time
 from hashlib import sha256
@@ -13,6 +12,7 @@ from shutil import copyfile, copytree, move, rmtree
 from tempfile import mkdtemp
 from typing import Any, Dict, List, Optional, Union
 
+import torch
 import yaml
 from huggingface_hub import HfFolder
 from pydantic.networks import AnyHttpUrl
@@ -42,7 +42,8 @@ from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMet
 from invokeai.backend.model_manager.probe import ModelProbe
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.util import InvokeAILogger
-from invokeai.backend.util.devices import choose_precision, choose_torch_device
+from invokeai.backend.util.catch_sigint import catch_sigint
+from invokeai.backend.util.devices import TorchDevice
 
 from .model_install_base import (
     MODEL_SOURCE_TO_TYPE_MAP,
@@ -111,17 +112,6 @@ class ModelInstallService(ModelInstallServiceBase):
     def start(self, invoker: Optional[Invoker] = None) -> None:
         """Start the installer thread."""
 
-        # Yes, this is weird. When the installer thread is running, the
-        # thread masks the ^C signal. When we receive a
-        # sigINT, we stop the thread, reset sigINT, and send a new
-        # sigINT to the parent process.
-        def sigint_handler(signum, frame):
-            self.stop()
-            signal.signal(signal.SIGINT, signal.SIG_DFL)
-            signal.raise_signal(signal.SIGINT)
-
-        signal.signal(signal.SIGINT, sigint_handler)
-
         with self._lock:
             if self._running:
                 raise Exception("Attempt to start the installer service twice")
@@ -131,7 +121,8 @@ class ModelInstallService(ModelInstallServiceBase):
             # In normal use, we do not want to scan the models directory - it should never have orphaned models.
             # We should only do the scan when the flag is set (which should only be set when testing).
             if self.app_config.scan_models_on_startup:
-                self._register_orphaned_models()
+                with catch_sigint():
+                    self._register_orphaned_models()
 
             # Check all models' paths and confirm they exist. A model could be missing if it was installed on a volume
             # that isn't currently mounted. In this case, we don't want to delete the model from the database, but we do
@@ -634,11 +625,10 @@ class ModelInstallService(ModelInstallServiceBase):
             self._next_job_id += 1
         return id
 
-    @staticmethod
-    def _guess_variant() -> Optional[ModelRepoVariant]:
+    def _guess_variant(self) -> Optional[ModelRepoVariant]:
         """Guess the best HuggingFace variant type to download."""
-        precision = choose_precision(choose_torch_device())
-        return ModelRepoVariant.FP16 if precision == "float16" else None
+        precision = TorchDevice.choose_torch_dtype()
+        return ModelRepoVariant.FP16 if precision == torch.float16 else None
 
     def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
         return ModelInstallJob(
@@ -754,6 +744,8 @@ class ModelInstallService(ModelInstallServiceBase):
             self._download_cache[download_job.source] = install_job  # matches a download job to an install job
             install_job.download_parts.add(download_job)
 
+        # only start the jobs once install_job.download_parts is fully populated
+        for download_job in install_job.download_parts:
             self._download_queue.submit_download_job(
                 download_job,
                 on_start=self._download_started_callback,
@@ -762,6 +754,7 @@ class ModelInstallService(ModelInstallServiceBase):
                 on_error=self._download_error_callback,
                 on_cancelled=self._download_cancelled_callback,
             )
+
         return install_job
 
     def _stat_size(self, path: Path) -> int:

invokeai/app/services/model_manager/model_manager_default.py

@@ -1,12 +1,14 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 """Implementation of ModelManagerServiceBase."""
 
+from typing import Optional
+
 import torch
 from typing_extensions import Self
 
 from invokeai.app.services.invoker import Invoker
 from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache, ModelLoaderRegistry
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 from ..config import InvokeAIAppConfig
@@ -67,7 +69,7 @@ class ModelManagerService(ModelManagerServiceBase):
         model_record_service: ModelRecordServiceBase,
         download_queue: DownloadQueueServiceBase,
         events: EventServiceBase,
-        execution_device: torch.device = choose_torch_device(),
+        execution_device: Optional[torch.device] = None,
     ) -> Self:
         """
         Construct the model manager service instance.
@@ -82,7 +84,7 @@ class ModelManagerService(ModelManagerServiceBase):
             max_vram_cache_size=app_config.vram,
             lazy_offloading=app_config.lazy_offload,
             logger=logger,
-            execution_device=execution_device,
+            execution_device=execution_device or TorchDevice.choose_torch_device(),
         )
         convert_cache = ModelConvertCache(cache_path=app_config.convert_cache_path, max_size=app_config.convert_cache)
         loader = ModelLoadService(

invokeai/app/services/object_serializer/object_serializer_disk.py

@@ -1,6 +1,6 @@
+import shutil
 import tempfile
 import typing
-from dataclasses import dataclass
 from pathlib import Path
 from typing import TYPE_CHECKING, Optional, TypeVar
 
@@ -17,12 +17,6 @@ if TYPE_CHECKING:
 T = TypeVar("T")
 
 
-@dataclass
-class DeleteAllResult:
-    deleted_count: int
-    freed_space_bytes: float
-
-
 class ObjectSerializerDisk(ObjectSerializerBase[T]):
     """Disk-backed storage for arbitrary python objects. Serialization is handled by `torch.save` and `torch.load`.
 
@@ -35,6 +29,12 @@ class ObjectSerializerDisk(ObjectSerializerBase[T]):
         self._ephemeral = ephemeral
         self._base_output_dir = output_dir
         self._base_output_dir.mkdir(parents=True, exist_ok=True)
+
+        if self._ephemeral:
+            # Remove dangling tempdirs that might have been left over from an earlier unplanned shutdown.
+            for temp_dir in filter(Path.is_dir, self._base_output_dir.glob("tmp*")):
+                shutil.rmtree(temp_dir)
+
         # Must specify `ignore_cleanup_errors` to avoid fatal errors during cleanup on Windows
         self._tempdir = (
             tempfile.TemporaryDirectory(dir=self._base_output_dir, ignore_cleanup_errors=True) if ephemeral else None

invokeai/backend/image_util/depth_anything/__init__.py

@@ -13,7 +13,7 @@ from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.download_with_progress import download_with_progress_bar
 from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
 from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 config = get_config()
@@ -56,7 +56,7 @@ class DepthAnythingDetector:
     def __init__(self) -> None:
         self.model = None
         self.model_size: Union[Literal["large", "base", "small"], None] = None
-        self.device = choose_torch_device()
+        self.device = TorchDevice.choose_torch_device()
 
     def load_model(self, model_size: Literal["large", "base", "small"] = "small"):
         DEPTH_ANYTHING_MODEL_PATH = config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"]
@@ -81,7 +81,7 @@ class DepthAnythingDetector:
             self.model.load_state_dict(torch.load(DEPTH_ANYTHING_MODEL_PATH.as_posix(), map_location="cpu"))
             self.model.eval()
 
-        self.model.to(choose_torch_device())
+        self.model.to(self.device)
         return self.model
 
     def __call__(self, image: Image.Image, resolution: int = 512) -> Image.Image:
@@ -94,7 +94,7 @@ class DepthAnythingDetector:
 
         image_height, image_width = np_image.shape[:2]
         np_image = transform({"image": np_image})["image"]
-        tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(choose_torch_device())
+        tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(self.device)
 
         with torch.no_grad():
             depth = self.model(tensor_image)

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -7,7 +7,7 @@ import onnxruntime as ort
 
 from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.download_with_progress import download_with_progress_bar
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 from .onnxdet import inference_detector
 from .onnxpose import inference_pose
@@ -28,9 +28,9 @@ config = get_config()
 
 class Wholebody:
     def __init__(self):
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
 
-        providers = ["CUDAExecutionProvider"] if device == "cuda" else ["CPUExecutionProvider"]
+        providers = ["CUDAExecutionProvider"] if device.type == "cuda" else ["CPUExecutionProvider"]
 
         DET_MODEL_PATH = config.models_path / DWPOSE_MODELS["yolox_l.onnx"]["local"]
         download_with_progress_bar("yolox_l.onnx", DWPOSE_MODELS["yolox_l.onnx"]["url"], DET_MODEL_PATH)

invokeai/backend/image_util/infill_methods/lama.py

@@ -8,7 +8,7 @@ from PIL import Image
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.download_with_progress import download_with_progress_bar
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 
 def norm_img(np_img):
@@ -29,7 +29,7 @@ def load_jit_model(url_or_path, device):
 
 class LaMA:
     def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
         model_location = get_config().models_path / "core/misc/lama/lama.pt"
 
         if not model_location.exists():

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -11,7 +11,7 @@ from cv2.typing import MatLike
 from tqdm import tqdm
 
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 
 """
 Adapted from https://github.com/xinntao/Real-ESRGAN/blob/master/realesrgan/utils.py
@@ -65,7 +65,7 @@ class RealESRGAN:
         self.pre_pad = pre_pad
         self.mod_scale: Optional[int] = None
         self.half = half
-        self.device = choose_torch_device()
+        self.device = TorchDevice.choose_torch_device()
 
         loadnet = torch.load(model_path, map_location=torch.device("cpu"))
 

invokeai/backend/image_util/safety_checker.py

@@ -13,7 +13,7 @@ from transformers import AutoFeatureExtractor
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config.config_default import get_config
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
@@ -51,7 +51,7 @@ class SafetyChecker:
         cls._load_safety_checker()
         if cls.safety_checker is None or cls.feature_extractor is None:
             return False
-        device = choose_torch_device()
+        device = TorchDevice.choose_torch_device()
         features = cls.feature_extractor([image], return_tensors="pt")
         features.to(device)
         cls.safety_checker.to(device)

invokeai/backend/model_manager/config.py

@@ -301,12 +301,12 @@ class MainConfigBase(ModelConfigBase):
     default_settings: Optional[MainModelDefaultSettings] = Field(
         description="Default settings for this model", default=None
     )
+    variant: ModelVariantType = ModelVariantType.Normal
 
 
 class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
     """Model config for main checkpoint models."""
 
-    variant: ModelVariantType = ModelVariantType.Normal
     prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
     upcast_attention: bool = False
 

invokeai/backend/model_manager/load/load_default.py

@@ -18,7 +18,7 @@ from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoad
 from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase, ModelLockerBase
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data, calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
-from invokeai.backend.util.devices import choose_torch_device, torch_dtype
+from invokeai.backend.util.devices import TorchDevice
 
 
 # TO DO: The loader is not thread safe!
@@ -37,7 +37,7 @@ class ModelLoader(ModelLoaderBase):
         self._logger = logger
         self._ram_cache = ram_cache
         self._convert_cache = convert_cache
-        self._torch_dtype = torch_dtype(choose_torch_device())
+        self._torch_dtype = TorchDevice.choose_torch_dtype()
 
     def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """

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

@@ -30,15 +30,12 @@ import torch
 
 from invokeai.backend.model_manager import AnyModel, SubModelType
 from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
-from invokeai.backend.util.devices import choose_torch_device
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 from .model_cache_base import CacheRecord, CacheStats, ModelCacheBase, ModelLockerBase
 from .model_locker import ModelLocker
 
-if choose_torch_device() == torch.device("mps"):
-    from torch import mps
-
 # Maximum size of the cache, in gigs
 # Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
 DEFAULT_MAX_CACHE_SIZE = 6.0
@@ -244,9 +241,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                     f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
                 )
 
-        torch.cuda.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
+        TorchDevice.empty_cache()
 
     def move_model_to_device(self, cache_entry: CacheRecord[AnyModel], target_device: torch.device) -> None:
         """Move model into the indicated device.
@@ -416,10 +411,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 self.stats.cleared = models_cleared
             gc.collect()
 
-        torch.cuda.empty_cache()
-        if choose_torch_device() == torch.device("mps"):
-            mps.empty_cache()
-
+        TorchDevice.empty_cache()
         self.logger.debug(f"After making room: cached_models={len(self._cached_models)}")
 
     def _delete_cache_entry(self, cache_entry: CacheRecord[AnyModel]) -> None:

invokeai/backend/model_manager/merge.py

@@ -17,7 +17,7 @@ from diffusers.utils import logging as dlogging
 
 from invokeai.app.services.model_install import ModelInstallServiceBase
 from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
-from invokeai.backend.util.devices import choose_torch_device, torch_dtype
+from invokeai.backend.util.devices import TorchDevice
 
 from . import (
     AnyModelConfig,
@@ -43,6 +43,7 @@ class ModelMerger(object):
         Initialize a ModelMerger object with the model installer.
         """
         self._installer = installer
+        self._dtype = TorchDevice.choose_torch_dtype()
 
     def merge_diffusion_models(
         self,
@@ -68,7 +69,7 @@ class ModelMerger(object):
             warnings.simplefilter("ignore")
             verbosity = dlogging.get_verbosity()
             dlogging.set_verbosity_error()
-            dtype = torch.float16 if variant == "fp16" else torch_dtype(choose_torch_device())
+            dtype = torch.float16 if variant == "fp16" else self._dtype
 
             # Note that checkpoint_merger will not work with downloaded HuggingFace fp16 models
             # until upstream https://github.com/huggingface/diffusers/pull/6670 is merged and released.
@@ -151,7 +152,7 @@ class ModelMerger(object):
         dump_path.mkdir(parents=True, exist_ok=True)
         dump_path = dump_path / merged_model_name
 
-        dtype = torch.float16 if variant == "fp16" else torch_dtype(choose_torch_device())
+        dtype = torch.float16 if variant == "fp16" else self._dtype
         merged_pipe.save_pretrained(dump_path.as_posix(), safe_serialization=True, torch_dtype=dtype, variant=variant)
 
         # register model and get its unique key

invokeai/backend/model_manager/starter_models.py

@@ -155,7 +155,7 @@ STARTER_MODELS: list[StarterModel] = [
     StarterModel(
         name="IP Adapter",
         base=BaseModelType.StableDiffusion1,
-        source="InvokeAI/ip_adapter_sd15",
+        source="https://huggingface.co/InvokeAI/ip_adapter_sd15/resolve/main/ip-adapter_sd15.safetensors",
         description="IP-Adapter for SD 1.5 models",
         type=ModelType.IPAdapter,
         dependencies=[ip_adapter_sd_image_encoder],
@@ -163,7 +163,7 @@ STARTER_MODELS: list[StarterModel] = [
     StarterModel(
         name="IP Adapter Plus",
         base=BaseModelType.StableDiffusion1,
-        source="InvokeAI/ip_adapter_plus_sd15",
+        source="https://huggingface.co/InvokeAI/ip_adapter_plus_sd15/resolve/main/ip-adapter-plus_sd15.safetensors",
         description="Refined IP-Adapter for SD 1.5 models",
         type=ModelType.IPAdapter,
         dependencies=[ip_adapter_sd_image_encoder],
@@ -171,7 +171,7 @@ STARTER_MODELS: list[StarterModel] = [
     StarterModel(
         name="IP Adapter Plus Face",
         base=BaseModelType.StableDiffusion1,
-        source="InvokeAI/ip_adapter_plus_face_sd15",
+        source="https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15/resolve/main/ip-adapter-plus-face_sd15.safetensors",
         description="Refined IP-Adapter for SD 1.5 models, adapted for faces",
         type=ModelType.IPAdapter,
         dependencies=[ip_adapter_sd_image_encoder],
@@ -179,7 +179,7 @@ STARTER_MODELS: list[StarterModel] = [
     StarterModel(
         name="IP Adapter SDXL",
         base=BaseModelType.StableDiffusionXL,
-        source="InvokeAI/ip_adapter_sdxl",
+        source="https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h/resolve/main/ip-adapter_sdxl_vit-h.safetensors",
         description="IP-Adapter for SDXL models",
         type=ModelType.IPAdapter,
         dependencies=[ip_adapter_sdxl_image_encoder],

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -21,14 +21,11 @@ from pydantic import Field
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.services.config.config_default import get_config
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    IPAdapterData,
-    TextConditioningData,
-)
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import IPAdapterData, TextConditioningData
 from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
+from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher, UNetIPAdapterData
 from invokeai.backend.util.attention import auto_detect_slice_size
-from invokeai.backend.util.devices import normalize_device
+from invokeai.backend.util.devices import TorchDevice
 
 
 @dataclass
@@ -258,7 +255,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if self.unet.device.type == "cpu" or self.unet.device.type == "mps":
             mem_free = psutil.virtual_memory().free
         elif self.unet.device.type == "cuda":
-            mem_free, _ = torch.cuda.mem_get_info(normalize_device(self.unet.device))
+            mem_free, _ = torch.cuda.mem_get_info(TorchDevice.normalize(self.unet.device))
         else:
             raise ValueError(f"unrecognized device {self.unet.device}")
         # input tensor of [1, 4, h/8, w/8]
@@ -394,8 +391,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         unet_attention_patcher = None
         self.use_ip_adapter = use_ip_adapter
         attn_ctx = nullcontext()
+
         if use_ip_adapter or use_regional_prompting:
-            ip_adapters = [ipa.ip_adapter_model for ipa in ip_adapter_data] if use_ip_adapter else None
+            ip_adapters: Optional[List[UNetIPAdapterData]] = (
+                [{"ip_adapter": ipa.ip_adapter_model, "target_blocks": ipa.target_blocks} for ipa in ip_adapter_data]
+                if use_ip_adapter
+                else None
+            )
             unet_attention_patcher = UNetAttentionPatcher(ip_adapters)
             attn_ctx = unet_attention_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
 

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -53,6 +53,7 @@ class IPAdapterData:
     ip_adapter_model: IPAdapter
     ip_adapter_conditioning: IPAdapterConditioningInfo
     mask: torch.Tensor
+    target_blocks: List[str]
 
     # Either a single weight applied to all steps, or a list of weights for each step.
     weight: Union[float, List[float]] = 1.0

invokeai/backend/stable_diffusion/diffusion/custom_atttention.py

@@ -1,4 +1,5 @@
-from typing import Optional
+from dataclasses import dataclass
+from typing import List, Optional, cast
 
 import torch
 import torch.nn.functional as F
@@ -9,6 +10,12 @@ from invokeai.backend.stable_diffusion.diffusion.regional_ip_data import Regiona
 from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
 
 
+@dataclass
+class IPAdapterAttentionWeights:
+    ip_adapter_weights: IPAttentionProcessorWeights
+    skip: bool
+
+
 class CustomAttnProcessor2_0(AttnProcessor2_0):
     """A custom implementation of AttnProcessor2_0 that supports additional Invoke features.
     This implementation is based on
@@ -20,7 +27,7 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
     def __init__(
         self,
-        ip_adapter_weights: Optional[list[IPAttentionProcessorWeights]] = None,
+        ip_adapter_attention_weights: Optional[List[IPAdapterAttentionWeights]] = None,
     ):
         """Initialize a CustomAttnProcessor2_0.
         Note: Arguments that are the same for all attention layers are passed to __call__(). Arguments that are
@@ -30,23 +37,22 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
                 for the i'th IP-Adapter.
         """
         super().__init__()
-        self._ip_adapter_weights = ip_adapter_weights
-
-    def _is_ip_adapter_enabled(self) -> bool:
-        return self._ip_adapter_weights is not None
+        self._ip_adapter_attention_weights = ip_adapter_attention_weights
 
     def __call__(
         self,
         attn: Attention,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        temb: Optional[torch.FloatTensor] = None,
-        # For regional prompting:
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        # For Regional Prompting:
         regional_prompt_data: Optional[RegionalPromptData] = None,
-        percent_through: Optional[torch.FloatTensor] = None,
+        percent_through: Optional[torch.Tensor] = None,
         # For IP-Adapter:
         regional_ip_data: Optional[RegionalIPData] = None,
+        *args,
+        **kwargs,
     ) -> torch.FloatTensor:
         """Apply attention.
         Args:
@@ -130,17 +136,19 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
         # Apply IP-Adapter conditioning.
         if is_cross_attention:
-            if self._is_ip_adapter_enabled():
+            if self._ip_adapter_attention_weights:
                 assert regional_ip_data is not None
                 ip_masks = regional_ip_data.get_masks(query_seq_len=query_seq_len)
+
                 assert (
                     len(regional_ip_data.image_prompt_embeds)
-                    == len(self._ip_adapter_weights)
+                    == len(self._ip_adapter_attention_weights)
                     == len(regional_ip_data.scales)
                     == ip_masks.shape[1]
                 )
+
                 for ipa_index, ipa_embed in enumerate(regional_ip_data.image_prompt_embeds):
-                    ipa_weights = self._ip_adapter_weights[ipa_index]
+                    ipa_weights = self._ip_adapter_attention_weights[ipa_index].ip_adapter_weights
                     ipa_scale = regional_ip_data.scales[ipa_index]
                     ip_mask = ip_masks[0, ipa_index, ...]
 
@@ -153,29 +161,33 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
                     # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
 
-                    ip_key = ipa_weights.to_k_ip(ip_hidden_states)
-                    ip_value = ipa_weights.to_v_ip(ip_hidden_states)
+                    if not self._ip_adapter_attention_weights[ipa_index].skip:
+                        ip_key = ipa_weights.to_k_ip(ip_hidden_states)
+                        ip_value = ipa_weights.to_v_ip(ip_hidden_states)
 
-                    # Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
+                        # Expected ip_key and ip_value shape:
+                        # (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
 
-                    ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-                    ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+                        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+                        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
-                    # Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
+                        # Expected ip_key and ip_value shape:
+                        # (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
 
-                    # TODO: add support for attn.scale when we move to Torch 2.1
-                    ip_hidden_states = F.scaled_dot_product_attention(
-                        query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
-                    )
+                        # TODO: add support for attn.scale when we move to Torch 2.1
+                        ip_hidden_states = F.scaled_dot_product_attention(
+                            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
+                        )
 
-                    # Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
+                        # Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
+                        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(
+                            batch_size, -1, attn.heads * head_dim
+                        )
 
-                    ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-                    ip_hidden_states = ip_hidden_states.to(query.dtype)
+                        ip_hidden_states = ip_hidden_states.to(query.dtype)
 
-                    # Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
-
-                    hidden_states = hidden_states + ipa_scale * ip_hidden_states * ip_mask
+                        # Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
+                        hidden_states = hidden_states + ipa_scale * ip_hidden_states * ip_mask
             else:
                 # If IP-Adapter is not enabled, then regional_ip_data should not be passed in.
                 assert regional_ip_data is None
@@ -188,11 +200,15 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         hidden_states = attn.to_out[1](hidden_states)
 
         if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
             hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
 
         if attn.residual_connection:
             hidden_states = hidden_states + residual
 
         hidden_states = hidden_states / attn.rescale_output_factor
+        # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+        # End of unmodified block from AttnProcessor2_0
 
-        return hidden_states
+        # casting torch.Tensor to torch.FloatTensor to avoid type issues
+        return cast(torch.FloatTensor, hidden_states)

invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py

@@ -1,17 +1,25 @@
 from contextlib import contextmanager
-from typing import Optional
+from typing import List, Optional, TypedDict
 
 from diffusers.models import UNet2DConditionModel
 
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.stable_diffusion.diffusion.custom_atttention import CustomAttnProcessor2_0
+from invokeai.backend.stable_diffusion.diffusion.custom_atttention import (
+    CustomAttnProcessor2_0,
+    IPAdapterAttentionWeights,
+)
+
+
+class UNetIPAdapterData(TypedDict):
+    ip_adapter: IPAdapter
+    target_blocks: List[str]
 
 
 class UNetAttentionPatcher:
     """A class for patching a UNet with CustomAttnProcessor2_0 attention layers."""
 
-    def __init__(self, ip_adapters: Optional[list[IPAdapter]]):
-        self._ip_adapters = ip_adapters
+    def __init__(self, ip_adapter_data: Optional[List[UNetIPAdapterData]]):
+        self._ip_adapters = ip_adapter_data
 
     def _prepare_attention_processors(self, unet: UNet2DConditionModel):
         """Prepare a dict of attention processors that can be injected into a unet, and load the IP-Adapter attention
@@ -26,9 +34,22 @@ class UNetAttentionPatcher:
                 attn_procs[name] = CustomAttnProcessor2_0()
             else:
                 # Collect the weights from each IP Adapter for the idx'th attention processor.
-                attn_procs[name] = CustomAttnProcessor2_0(
-                    [ip_adapter.attn_weights.get_attention_processor_weights(idx) for ip_adapter in self._ip_adapters],
-                )
+                ip_adapter_attention_weights_collection: list[IPAdapterAttentionWeights] = []
+
+                for ip_adapter in self._ip_adapters:
+                    ip_adapter_weights = ip_adapter["ip_adapter"].attn_weights.get_attention_processor_weights(idx)
+                    skip = True
+                    for block in ip_adapter["target_blocks"]:
+                        if block in name:
+                            skip = False
+                            break
+                    ip_adapter_attention_weights: IPAdapterAttentionWeights = IPAdapterAttentionWeights(
+                        ip_adapter_weights=ip_adapter_weights, skip=skip
+                    )
+                    ip_adapter_attention_weights_collection.append(ip_adapter_attention_weights)
+
+                attn_procs[name] = CustomAttnProcessor2_0(ip_adapter_attention_weights_collection)
+
         return attn_procs
 
     @contextmanager

invokeai/backend/util/__init__.py

@@ -2,7 +2,6 @@
 Initialization file for invokeai.backend.util
 """
 
-from .devices import choose_precision, choose_torch_device
 from .logging import InvokeAILogger
 from .util import GIG, Chdir, directory_size
 
@@ -11,6 +10,4 @@ __all__ = [
     "directory_size",
     "Chdir",
     "InvokeAILogger",
-    "choose_precision",
-    "choose_torch_device",
 ]

invokeai/backend/util/catch_sigint.py

@@ -0,0 +1,29 @@
+"""
+This module defines a context manager `catch_sigint()` which temporarily replaces
+the sigINT handler defined by the ASGI in order to allow the user to ^C the application
+and shut it down immediately. This was implemented in order to allow the user to interrupt
+slow model hashing during startup.
+
+Use like this:
+
+  from invokeai.backend.util.catch_sigint import catch_sigint
+  with catch_sigint():
+      run_some_hard_to_interrupt_process()
+"""
+
+import signal
+from contextlib import contextmanager
+from typing import Generator
+
+
+def sigint_handler(signum, frame):  # type: ignore
+    signal.signal(signal.SIGINT, signal.SIG_DFL)
+    signal.raise_signal(signal.SIGINT)
+
+
+@contextmanager
+def catch_sigint() -> Generator[None, None, None]:
+    original_handler = signal.getsignal(signal.SIGINT)
+    signal.signal(signal.SIGINT, sigint_handler)
+    yield
+    signal.signal(signal.SIGINT, original_handler)

invokeai/backend/util/devices.py

@@ -1,89 +1,110 @@
-from __future__ import annotations
-
-from contextlib import nullcontext
-from typing import Literal, Optional, Union
+from typing import Dict, Literal, Optional, Union
 
 import torch
-from torch import autocast
+from deprecated import deprecated
 
-from invokeai.app.services.config.config_default import PRECISION, get_config
+from invokeai.app.services.config.config_default import get_config
 
+# legacy APIs
+TorchPrecisionNames = Literal["float32", "float16", "bfloat16"]
 CPU_DEVICE = torch.device("cpu")
 CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
 
 
+@deprecated("Use TorchDevice.choose_torch_dtype() instead.")  # type: ignore
+def choose_precision(device: torch.device) -> TorchPrecisionNames:
+    """Return the string representation of the recommended torch device."""
+    torch_dtype = TorchDevice.choose_torch_dtype(device)
+    return PRECISION_TO_NAME[torch_dtype]
+
+
+@deprecated("Use TorchDevice.choose_torch_device() instead.")  # type: ignore
 def choose_torch_device() -> torch.device:
-    """Convenience routine for guessing which GPU device to run model on"""
-    config = get_config()
-    if config.device == "auto":
-        if torch.cuda.is_available():
-            return torch.device("cuda")
-        if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-            return torch.device("mps")
+    """Return the torch.device to use for accelerated inference."""
+    return TorchDevice.choose_torch_device()
+
+
+@deprecated("Use TorchDevice.choose_torch_dtype() instead.")  # type: ignore
+def torch_dtype(device: torch.device) -> torch.dtype:
+    """Return the torch precision for the recommended torch device."""
+    return TorchDevice.choose_torch_dtype(device)
+
+
+NAME_TO_PRECISION: Dict[TorchPrecisionNames, torch.dtype] = {
+    "float32": torch.float32,
+    "float16": torch.float16,
+    "bfloat16": torch.bfloat16,
+}
+PRECISION_TO_NAME: Dict[torch.dtype, TorchPrecisionNames] = {v: k for k, v in NAME_TO_PRECISION.items()}
+
+
+class TorchDevice:
+    """Abstraction layer for torch devices."""
+
+    @classmethod
+    def choose_torch_device(cls) -> torch.device:
+        """Return the torch.device to use for accelerated inference."""
+        app_config = get_config()
+        if app_config.device != "auto":
+            device = torch.device(app_config.device)
+        elif torch.cuda.is_available():
+            device = CUDA_DEVICE
+        elif torch.backends.mps.is_available():
+            device = MPS_DEVICE
         else:
-            return CPU_DEVICE
-    else:
-        return torch.device(config.device)
+            device = CPU_DEVICE
+        return cls.normalize(device)
 
+    @classmethod
+    def choose_torch_dtype(cls, device: Optional[torch.device] = None) -> torch.dtype:
+        """Return the precision to use for accelerated inference."""
+        device = device or cls.choose_torch_device()
+        config = get_config()
+        if device.type == "cuda" and torch.cuda.is_available():
+            device_name = torch.cuda.get_device_name(device)
+            if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
+                # These GPUs have limited support for float16
+                return cls._to_dtype("float32")
+            elif config.precision == "auto":
+                # Default to float16 for CUDA devices
+                return cls._to_dtype("float16")
+            else:
+                # Use the user-defined precision
+                return cls._to_dtype(config.precision)
 
-def get_torch_device_name() -> str:
-    device = choose_torch_device()
-    return torch.cuda.get_device_name(device) if device.type == "cuda" else device.type.upper()
+        elif device.type == "mps" and torch.backends.mps.is_available():
+            if config.precision == "auto":
+                # Default to float16 for MPS devices
+                return cls._to_dtype("float16")
+            else:
+                # Use the user-defined precision
+                return cls._to_dtype(config.precision)
+        # CPU / safe fallback
+        return cls._to_dtype("float32")
 
+    @classmethod
+    def get_torch_device_name(cls) -> str:
+        """Return the device name for the current torch device."""
+        device = cls.choose_torch_device()
+        return torch.cuda.get_device_name(device) if device.type == "cuda" else device.type.upper()
 
-def choose_precision(device: torch.device) -> Literal["float32", "float16", "bfloat16"]:
-    """Return an appropriate precision for the given torch device."""
-    app_config = get_config()
-    if device.type == "cuda":
-        device_name = torch.cuda.get_device_name(device)
-        if "GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name:
-            # These GPUs have limited support for float16
-            return "float32"
-        elif app_config.precision == "auto" or app_config.precision == "autocast":
-            # Default to float16 for CUDA devices
-            return "float16"
-        else:
-            # Use the user-defined precision
-            return app_config.precision
-    elif device.type == "mps":
-        if app_config.precision == "auto" or app_config.precision == "autocast":
-            # Default to float16 for MPS devices
-            return "float16"
-        else:
-            # Use the user-defined precision
-            return app_config.precision
-    # CPU / safe fallback
-    return "float32"
-
-
-def torch_dtype(device: Optional[torch.device] = None) -> torch.dtype:
-    device = device or choose_torch_device()
-    precision = choose_precision(device)
-    if precision == "float16":
-        return torch.float16
-    if precision == "bfloat16":
-        return torch.bfloat16
-    else:
-        # "auto", "autocast", "float32"
-        return torch.float32
-
-
-def choose_autocast(precision: PRECISION):
-    """Returns an autocast context or nullcontext for the given precision string"""
-    # float16 currently requires autocast to avoid errors like:
-    # 'expected scalar type Half but found Float'
-    if precision == "autocast" or precision == "float16":
-        return autocast
-    return nullcontext
-
-
-def normalize_device(device: Union[str, torch.device]) -> torch.device:
-    """Ensure device has a device index defined, if appropriate."""
-    device = torch.device(device)
-    if device.index is None:
-        # cuda might be the only torch backend that currently uses the device index?
-        # I don't see anything like `current_device` for cpu or mps.
-        if device.type == "cuda":
+    @classmethod
+    def normalize(cls, device: Union[str, torch.device]) -> torch.device:
+        """Add the device index to CUDA devices."""
+        device = torch.device(device)
+        if device.index is None and device.type == "cuda" and torch.cuda.is_available():
             device = torch.device(device.type, torch.cuda.current_device())
-    return device
+        return device
+
+    @classmethod
+    def empty_cache(cls) -> None:
+        """Clear the GPU device cache."""
+        if torch.backends.mps.is_available():
+            torch.mps.empty_cache()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+    @classmethod
+    def _to_dtype(cls, precision_name: TorchPrecisionNames) -> torch.dtype:
+        return NAME_TO_PRECISION[precision_name]

invokeai/frontend/web/.storybook/preview.tsx

@@ -11,6 +11,7 @@ import { createStore } from '../src/app/store/store';
 // @ts-ignore
 import translationEN from '../public/locales/en.json';
 import { ReduxInit } from './ReduxInit';
+import { $store } from 'app/store/nanostores/store';
 
 i18n.use(initReactI18next).init({
   lng: 'en',
@@ -25,6 +26,7 @@ i18n.use(initReactI18next).init({
 });
 
 const store = createStore(undefined, false);
+$store.set(store);
 $baseUrl.set('http://localhost:9090');
 
 const preview: Preview = {

invokeai/frontend/web/package.json

@@ -25,7 +25,7 @@
     "typegen": "node scripts/typegen.js",
     "preview": "vite preview",
     "lint:knip": "knip",
-    "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:1 src/main.tsx",
+    "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:0 src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",
     "lint:prettier": "prettier --check .",
     "lint:tsc": "tsc --noEmit",
@@ -95,6 +95,7 @@
     "reactflow": "^11.10.4",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
+    "redux-undo": "^1.1.0",
     "rfdc": "^1.3.1",
     "roarr": "^7.21.1",
     "serialize-error": "^11.0.3",

invokeai/frontend/web/pnpm-lock.yaml

@@ -140,6 +140,9 @@ dependencies:
   redux-remember:
     specifier: ^5.1.0
     version: 5.1.0(redux@5.0.1)
+  redux-undo:
+    specifier: ^1.1.0
+    version: 1.1.0
   rfdc:
     specifier: ^1.3.1
     version: 1.3.1
@@ -11962,6 +11965,10 @@ packages:
       redux: 5.0.1
     dev: false
 
+  /redux-undo@1.1.0:
+    resolution: {integrity: sha512-zzLFh2qeF0MTIlzDhDLm9NtkfBqCllQJ3OCuIl5RKlG/ayHw6GUdIFdMhzMS9NnrnWdBX5u//ExMOHpfudGGOg==}
+    dev: false
+
   /redux@5.0.1:
     resolution: {integrity: sha512-M9/ELqF6fy8FwmkpnF0S3YKOqMyoWJ4+CS5Efg2ct3oY9daQvd/Pc71FpGZsVsbl3Cpb+IIcjBDUnnyBdQbq4w==}
     dev: false

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

@@ -85,7 +85,8 @@
         "loadMore": "Mehr laden",
         "noImagesInGallery": "Keine Bilder in der Galerie",
         "loading": "Lade",
-        "deleteImage": "Lösche Bild",
+        "deleteImage_one": "Lösche Bild",
+        "deleteImage_other": "",
         "copy": "Kopieren",
         "download": "Runterladen",
         "setCurrentImage": "Setze aktuelle Bild",

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

@@ -69,6 +69,7 @@
         "auto": "Auto",
         "back": "Back",
         "batch": "Batch Manager",
+        "beta": "Beta",
         "cancel": "Cancel",
         "copy": "Copy",
         "copyError": "$t(gallery.copy) Error",
@@ -83,6 +84,8 @@
         "direction": "Direction",
         "ipAdapter": "IP Adapter",
         "t2iAdapter": "T2I Adapter",
+        "positivePrompt": "Positive Prompt",
+        "negativePrompt": "Negative Prompt",
         "discordLabel": "Discord",
         "dontAskMeAgain": "Don't ask me again",
         "error": "Error",
@@ -135,7 +138,9 @@
         "red": "Red",
         "green": "Green",
         "blue": "Blue",
-        "alpha": "Alpha"
+        "alpha": "Alpha",
+        "selected": "Selected",
+        "viewer": "Viewer"
     },
     "controlnet": {
         "controlAdapter_one": "Control Adapter",
@@ -213,6 +218,10 @@
         "resize": "Resize",
         "resizeSimple": "Resize (Simple)",
         "resizeMode": "Resize Mode",
+        "ipAdapterMethod": "Method",
+        "full": "Full",
+        "style": "Style Only",
+        "composition": "Composition Only",
         "safe": "Safe",
         "saveControlImage": "Save Control Image",
         "scribble": "scribble",
@@ -770,6 +779,8 @@
         "float": "Float",
         "fullyContainNodes": "Fully Contain Nodes to Select",
         "fullyContainNodesHelp": "Nodes must be fully inside the selection box to be selected",
+        "showEdgeLabels": "Show Edge Labels",
+        "showEdgeLabelsHelp": "Show labels on edges, indicating the connected nodes",
         "hideLegendNodes": "Hide Field Type Legend",
         "hideMinimapnodes": "Hide MiniMap",
         "inputMayOnlyHaveOneConnection": "Input may only have one connection",
@@ -886,6 +897,7 @@
         "denoisingStrength": "Denoising Strength",
         "downloadImage": "Download Image",
         "general": "General",
+        "globalSettings": "Global Settings",
         "height": "Height",
         "imageFit": "Fit Initial Image To Output Size",
         "images": "Images",
@@ -1176,6 +1188,10 @@
             "heading": "Resize Mode",
             "paragraphs": ["Method to fit Control Adapter's input image size to the output generation size."]
         },
+        "ipAdapterMethod": {
+            "heading": "Method",
+            "paragraphs": ["Method by which to apply the current IP Adapter."]
+        },
         "controlNetWeight": {
             "heading": "Weight",
             "paragraphs": [
@@ -1494,5 +1510,27 @@
     },
     "app": {
         "storeNotInitialized": "Store is not initialized"
+    },
+    "regionalPrompts": {
+        "deleteAll": "Delete All",
+        "addLayer": "Add Layer",
+        "moveToFront": "Move to Front",
+        "moveToBack": "Move to Back",
+        "moveForward": "Move Forward",
+        "moveBackward": "Move Backward",
+        "brushSize": "Brush Size",
+        "regionalControl": "Regional Control (ALPHA)",
+        "enableRegionalPrompts": "Enable $t(regionalPrompts.regionalPrompts)",
+        "globalMaskOpacity": "Global Mask Opacity",
+        "autoNegative": "Auto Negative",
+        "toggleVisibility": "Toggle Layer Visibility",
+        "deletePrompt": "Delete Prompt",
+        "resetRegion": "Reset Region",
+        "debugLayers": "Debug Layers",
+        "rectangle": "Rectangle",
+        "maskPreviewColor": "Mask Preview Color",
+        "addPositivePrompt": "Add $t(common.positivePrompt)",
+        "addNegativePrompt": "Add $t(common.negativePrompt)",
+        "addIPAdapter": "Add $t(common.ipAdapter)"
     }
 }

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

@@ -33,7 +33,9 @@
         "autoSwitchNewImages": "Auto seleccionar Imágenes nuevas",
         "loadMore": "Cargar más",
         "noImagesInGallery": "No hay imágenes para mostrar",
-        "deleteImage": "Eliminar Imagen",
+        "deleteImage_one": "Eliminar Imagen",
+        "deleteImage_many": "",
+        "deleteImage_other": "",
         "deleteImageBin": "Las imágenes eliminadas se enviarán a la papelera de tu sistema operativo.",
         "deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
         "assets": "Activos",

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

@@ -82,7 +82,9 @@
         "autoSwitchNewImages": "Passaggio automatico a nuove immagini",
         "loadMore": "Carica altro",
         "noImagesInGallery": "Nessuna immagine da visualizzare",
-        "deleteImage": "Elimina l'immagine",
+        "deleteImage_one": "Elimina l'immagine",
+        "deleteImage_many": "Elimina {{count}} immagini",
+        "deleteImage_other": "Elimina {{count}} immagini",
         "deleteImagePermanent": "Le immagini eliminate non possono essere ripristinate.",
         "deleteImageBin": "Le immagini eliminate verranno spostate nel cestino del tuo sistema operativo.",
         "assets": "Risorse",

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

@@ -90,7 +90,7 @@
         "problemDeletingImages": "画像の削除中に問題が発生",
         "drop": "ドロップ",
         "dropOrUpload": "$t(gallery.drop) またはアップロード",
-        "deleteImage": "画像を削除",
+        "deleteImage_other": "画像を削除",
         "deleteImageBin": "削除された画像はOSのゴミ箱に送られます。",
         "deleteImagePermanent": "削除された画像は復元できません。",
         "download": "ダウンロード",

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

@@ -82,7 +82,7 @@
         "drop": "드랍",
         "problemDeletingImages": "이미지 삭제 중 발생한 문제",
         "downloadSelection": "선택 항목 다운로드",
-        "deleteImage": "이미지 삭제",
+        "deleteImage_other": "이미지 삭제",
         "currentlyInUse": "이 이미지는 현재 다음 기능에서 사용되고 있습니다:",
         "dropOrUpload": "$t(gallery.drop) 또는 업로드",
         "copy": "복사",

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

@@ -42,7 +42,8 @@
         "autoSwitchNewImages": "Wissel autom. naar nieuwe afbeeldingen",
         "loadMore": "Laad meer",
         "noImagesInGallery": "Geen afbeeldingen om te tonen",
-        "deleteImage": "Verwijder afbeelding",
+        "deleteImage_one": "Verwijder afbeelding",
+        "deleteImage_other": "",
         "deleteImageBin": "Verwijderde afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.",
         "deleteImagePermanent": "Verwijderde afbeeldingen kunnen niet worden hersteld.",
         "assets": "Eigen onderdelen",

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

@@ -86,7 +86,9 @@
         "noImagesInGallery": "Изображений нет",
         "deleteImagePermanent": "Удаленные изображения невозможно восстановить.",
         "deleteImageBin": "Удаленные изображения будут отправлены в корзину вашей операционной системы.",
-        "deleteImage": "Удалить изображение",
+        "deleteImage_one": "Удалить изображение",
+        "deleteImage_few": "",
+        "deleteImage_many": "",
         "assets": "Ресурсы",
         "autoAssignBoardOnClick": "Авто-назначение доски по клику",
         "deleteSelection": "Удалить выделенное",

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

@@ -298,7 +298,8 @@
         "noImagesInGallery": "Gösterilecek Görsel Yok",
         "autoSwitchNewImages": "Yeni Görseli Biter Bitmez Gör",
         "currentlyInUse": "Bu görsel şurada kullanımda:",
-        "deleteImage": "Görseli Sil",
+        "deleteImage_one": "Görseli Sil",
+        "deleteImage_other": "",
         "loadMore": "Daha Getir",
         "setCurrentImage": "Çalışma Görseli Yap",
         "unableToLoad": "Galeri Yüklenemedi",

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

@@ -78,7 +78,7 @@
         "autoSwitchNewImages": "自动切换到新图像",
         "loadMore": "加载更多",
         "noImagesInGallery": "无图像可用于显示",
-        "deleteImage": "删除图片",
+        "deleteImage_other": "删除图片",
         "deleteImageBin": "被删除的图片会发送到你操作系统的回收站。",
         "deleteImagePermanent": "删除的图片无法被恢复。",
         "assets": "素材",

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

@@ -27,7 +27,8 @@ export type LoggerNamespace =
   | 'socketio'
   | 'session'
   | 'queue'
-  | 'dnd';
+  | 'dnd'
+  | 'regionalPrompts';
 
 export const logger = (namespace: LoggerNamespace) => $logger.get().child({ namespace });
 

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

@@ -21,6 +21,11 @@ import { workflowPersistConfig, workflowSlice } from 'features/nodes/store/workf
 import { generationPersistConfig, generationSlice } from 'features/parameters/store/generationSlice';
 import { postprocessingPersistConfig, postprocessingSlice } from 'features/parameters/store/postprocessingSlice';
 import { queueSlice } from 'features/queue/store/queueSlice';
+import {
+  regionalPromptsPersistConfig,
+  regionalPromptsSlice,
+  regionalPromptsUndoableConfig,
+} from 'features/regionalPrompts/store/regionalPromptsSlice';
 import { sdxlPersistConfig, sdxlSlice } from 'features/sdxl/store/sdxlSlice';
 import { configSlice } from 'features/system/store/configSlice';
 import { systemPersistConfig, systemSlice } from 'features/system/store/systemSlice';
@@ -30,6 +35,7 @@ import { defaultsDeep, keys, omit, pick } from 'lodash-es';
 import dynamicMiddlewares from 'redux-dynamic-middlewares';
 import type { SerializeFunction, UnserializeFunction } from 'redux-remember';
 import { rememberEnhancer, rememberReducer } from 'redux-remember';
+import undoable from 'redux-undo';
 import { serializeError } from 'serialize-error';
 import { api } from 'services/api';
 import { authToastMiddleware } from 'services/api/authToastMiddleware';
@@ -59,6 +65,7 @@ const allReducers = {
   [queueSlice.name]: queueSlice.reducer,
   [workflowSlice.name]: workflowSlice.reducer,
   [hrfSlice.name]: hrfSlice.reducer,
+  [regionalPromptsSlice.name]: undoable(regionalPromptsSlice.reducer, regionalPromptsUndoableConfig),
   [api.reducerPath]: api.reducer,
 };
 
@@ -103,6 +110,7 @@ const persistConfigs: { [key in keyof typeof allReducers]?: PersistConfig } = {
   [loraPersistConfig.name]: loraPersistConfig,
   [modelManagerV2PersistConfig.name]: modelManagerV2PersistConfig,
   [hrfPersistConfig.name]: hrfPersistConfig,
+  [regionalPromptsPersistConfig.name]: regionalPromptsPersistConfig,
 };
 
 const unserialize: UnserializeFunction = (data, key) => {
@@ -114,6 +122,7 @@ const unserialize: UnserializeFunction = (data, key) => {
   try {
     const { initialState, migrate } = persistConfig;
     const parsed = JSON.parse(data);
+
     // strip out old keys
     const stripped = pick(parsed, keys(initialState));
     // run (additive) migrations
@@ -141,7 +150,9 @@ const serialize: SerializeFunction = (data, key) => {
   if (!persistConfig) {
     throw new Error(`No persist config for slice "${key}"`);
   }
-  const result = omit(data, persistConfig.persistDenylist);
+  // Heuristic to determine if the slice is undoable - could just hardcode it in the persistConfig
+  const isUndoable = 'present' in data && 'past' in data && 'future' in data && '_latestUnfiltered' in data;
+  const result = omit(isUndoable ? data.present : data, persistConfig.persistDenylist);
   return JSON.stringify(result);
 };
 

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

@@ -26,7 +26,7 @@ const sx: ChakraProps['sx'] = {
 
 const colorPickerStyles: CSSProperties = { width: '100%' };
 
-const numberInputWidth: ChakraProps['w'] = '4.2rem';
+const numberInputWidth: ChakraProps['w'] = '3.5rem';
 
 const IAIColorPicker = (props: IAIColorPickerProps) => {
   const { color, onChange, withNumberInput, ...rest } = props;
@@ -41,7 +41,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
       {withNumberInput && (
         <Flex gap={5}>
           <FormControl gap={0}>
-            <FormLabel>{t('common.red')}</FormLabel>
+            <FormLabel>{t('common.red')[0]}</FormLabel>
             <CompositeNumberInput
               value={color.r}
               onChange={handleChangeR}
@@ -53,7 +53,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
             />
           </FormControl>
           <FormControl gap={0}>
-            <FormLabel>{t('common.green')}</FormLabel>
+            <FormLabel>{t('common.green')[0]}</FormLabel>
             <CompositeNumberInput
               value={color.g}
               onChange={handleChangeG}
@@ -65,7 +65,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
             />
           </FormControl>
           <FormControl gap={0}>
-            <FormLabel>{t('common.blue')}</FormLabel>
+            <FormLabel>{t('common.blue')[0]}</FormLabel>
             <CompositeNumberInput
               value={color.b}
               onChange={handleChangeB}
@@ -77,7 +77,7 @@ const IAIColorPicker = (props: IAIColorPickerProps) => {
             />
           </FormControl>
           <FormControl gap={0}>
-            <FormLabel>{t('common.alpha')}</FormLabel>
+            <FormLabel>{t('common.alpha')[0]}</FormLabel>
             <CompositeNumberInput
               value={color.a}
               onChange={handleChangeA}

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

@@ -24,6 +24,7 @@ export type Feature =
   | 'dynamicPromptsSeedBehaviour'
   | 'imageFit'
   | 'infillMethod'
+  | 'ipAdapterMethod'
   | 'lora'
   | 'loraWeight'
   | 'noiseUseCPU'

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

@@ -0,0 +1,84 @@
+import type { ChakraProps } from '@invoke-ai/ui-library';
+import { CompositeNumberInput, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import type { CSSProperties } from 'react';
+import { memo, useCallback } from 'react';
+import { RgbColorPicker as ColorfulRgbColorPicker } from 'react-colorful';
+import type { ColorPickerBaseProps, RgbColor } from 'react-colorful/dist/types';
+import { useTranslation } from 'react-i18next';
+
+type RgbColorPickerProps = ColorPickerBaseProps<RgbColor> & {
+  withNumberInput?: boolean;
+};
+
+const colorPickerPointerStyles: NonNullable<ChakraProps['sx']> = {
+  width: 6,
+  height: 6,
+  borderColor: 'base.100',
+};
+
+const sx: ChakraProps['sx'] = {
+  '.react-colorful__hue-pointer': colorPickerPointerStyles,
+  '.react-colorful__saturation-pointer': colorPickerPointerStyles,
+  '.react-colorful__alpha-pointer': colorPickerPointerStyles,
+  gap: 5,
+  flexDir: 'column',
+};
+
+const colorPickerStyles: CSSProperties = { width: '100%' };
+
+const numberInputWidth: ChakraProps['w'] = '3.5rem';
+
+const RgbColorPicker = (props: RgbColorPickerProps) => {
+  const { color, onChange, withNumberInput, ...rest } = props;
+  const { t } = useTranslation();
+  const handleChangeR = useCallback((r: number) => onChange({ ...color, r }), [color, onChange]);
+  const handleChangeG = useCallback((g: number) => onChange({ ...color, g }), [color, onChange]);
+  const handleChangeB = useCallback((b: number) => onChange({ ...color, b }), [color, onChange]);
+  return (
+    <Flex sx={sx}>
+      <ColorfulRgbColorPicker color={color} onChange={onChange} style={colorPickerStyles} {...rest} />
+      {withNumberInput && (
+        <Flex gap={5}>
+          <FormControl gap={0}>
+            <FormLabel>{t('common.red')[0]}</FormLabel>
+            <CompositeNumberInput
+              value={color.r}
+              onChange={handleChangeR}
+              min={0}
+              max={255}
+              step={1}
+              w={numberInputWidth}
+              defaultValue={90}
+            />
+          </FormControl>
+          <FormControl gap={0}>
+            <FormLabel>{t('common.green')[0]}</FormLabel>
+            <CompositeNumberInput
+              value={color.g}
+              onChange={handleChangeG}
+              min={0}
+              max={255}
+              step={1}
+              w={numberInputWidth}
+              defaultValue={90}
+            />
+          </FormControl>
+          <FormControl gap={0}>
+            <FormLabel>{t('common.blue')[0]}</FormLabel>
+            <CompositeNumberInput
+              value={color.b}
+              onChange={handleChangeB}
+              min={0}
+              max={255}
+              step={1}
+              w={numberInputWidth}
+              defaultValue={255}
+            />
+          </FormControl>
+        </Flex>
+      )}
+    </Flex>
+  );
+};
+
+export default memo(RgbColorPicker);

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

@@ -9,7 +9,7 @@ import { useHotkeys } from 'react-hotkeys-hook';
 
 export const useGlobalHotkeys = () => {
   const dispatch = useAppDispatch();
-  const isModelManagerEnabled = useFeatureStatus('modelManager').isFeatureEnabled;
+  const isModelManagerEnabled = useFeatureStatus('modelManager');
   const { queueBack, isDisabled: isDisabledQueueBack, isLoading: isLoadingQueueBack } = useQueueBack();
 
   useHotkeys(

invokeai/frontend/web/src/common/util/arrayUtils.test.ts

@@ -0,0 +1,85 @@
+import { moveBackward, moveForward, moveToBack, moveToFront } from 'common/util/arrayUtils';
+import { describe, expect, it } from 'vitest';
+
+describe('Array Manipulation Functions', () => {
+  const originalArray = ['a', 'b', 'c', 'd'];
+  describe('moveForwardOne', () => {
+    it('should move an item forward by one position', () => {
+      const array = [...originalArray];
+      const result = moveForward(array, (item) => item === 'b');
+      expect(result).toEqual(['a', 'c', 'b', 'd']);
+    });
+
+    it('should do nothing if the item is at the end', () => {
+      const array = [...originalArray];
+      const result = moveForward(array, (item) => item === 'd');
+      expect(result).toEqual(['a', 'b', 'c', 'd']);
+    });
+
+    it("should leave the array unchanged if the item isn't in the array", () => {
+      const array = [...originalArray];
+      const result = moveForward(array, (item) => item === 'z');
+      expect(result).toEqual(originalArray);
+    });
+  });
+
+  describe('moveToFront', () => {
+    it('should move an item to the front', () => {
+      const array = [...originalArray];
+      const result = moveToFront(array, (item) => item === 'c');
+      expect(result).toEqual(['c', 'a', 'b', 'd']);
+    });
+
+    it('should do nothing if the item is already at the front', () => {
+      const array = [...originalArray];
+      const result = moveToFront(array, (item) => item === 'a');
+      expect(result).toEqual(['a', 'b', 'c', 'd']);
+    });
+
+    it("should leave the array unchanged if the item isn't in the array", () => {
+      const array = [...originalArray];
+      const result = moveToFront(array, (item) => item === 'z');
+      expect(result).toEqual(originalArray);
+    });
+  });
+
+  describe('moveBackwardsOne', () => {
+    it('should move an item backward by one position', () => {
+      const array = [...originalArray];
+      const result = moveBackward(array, (item) => item === 'c');
+      expect(result).toEqual(['a', 'c', 'b', 'd']);
+    });
+
+    it('should do nothing if the item is at the beginning', () => {
+      const array = [...originalArray];
+      const result = moveBackward(array, (item) => item === 'a');
+      expect(result).toEqual(['a', 'b', 'c', 'd']);
+    });
+
+    it("should leave the array unchanged if the item isn't in the array", () => {
+      const array = [...originalArray];
+      const result = moveBackward(array, (item) => item === 'z');
+      expect(result).toEqual(originalArray);
+    });
+  });
+
+  describe('moveToBack', () => {
+    it('should move an item to the back', () => {
+      const array = [...originalArray];
+      const result = moveToBack(array, (item) => item === 'b');
+      expect(result).toEqual(['a', 'c', 'd', 'b']);
+    });
+
+    it('should do nothing if the item is already at the back', () => {
+      const array = [...originalArray];
+      const result = moveToBack(array, (item) => item === 'd');
+      expect(result).toEqual(['a', 'b', 'c', 'd']);
+    });
+
+    it("should leave the array unchanged if the item isn't in the array", () => {
+      const array = [...originalArray];
+      const result = moveToBack(array, (item) => item === 'z');
+      expect(result).toEqual(originalArray);
+    });
+  });
+});

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

@@ -0,0 +1,37 @@
+export const moveForward = <T>(array: T[], callback: (item: T) => boolean): T[] => {
+  const index = array.findIndex(callback);
+  if (index >= 0 && index < array.length - 1) {
+    //@ts-expect-error - These indicies are safe per the previous check
+    [array[index], array[index + 1]] = [array[index + 1], array[index]];
+  }
+  return array;
+};
+
+export const moveToFront = <T>(array: T[], callback: (item: T) => boolean): T[] => {
+  const index = array.findIndex(callback);
+  if (index > 0) {
+    const [item] = array.splice(index, 1);
+    //@ts-expect-error - These indicies are safe per the previous check
+    array.unshift(item);
+  }
+  return array;
+};
+
+export const moveBackward = <T>(array: T[], callback: (item: T) => boolean): T[] => {
+  const index = array.findIndex(callback);
+  if (index > 0) {
+    //@ts-expect-error - These indicies are safe per the previous check
+    [array[index], array[index - 1]] = [array[index - 1], array[index]];
+  }
+  return array;
+};
+
+export const moveToBack = <T>(array: T[], callback: (item: T) => boolean): T[] => {
+  const index = array.findIndex(callback);
+  if (index >= 0 && index < array.length - 1) {
+    const [item] = array.splice(index, 1);
+    //@ts-expect-error - These indicies are safe per the previous check
+    array.push(item);
+  }
+  return array;
+};

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

@@ -10,6 +10,18 @@ import { clamp } from 'lodash-es';
 import type { MutableRefObject } from 'react';
 import { useCallback } from 'react';
 
+export const calculateNewBrushSize = (brushSize: number, delta: number) => {
+  // This equation was derived by fitting a curve to the desired brush sizes and deltas
+  // see https://github.com/invoke-ai/InvokeAI/pull/5542#issuecomment-1915847565
+  const targetDelta = Math.sign(delta) * 0.7363 * Math.pow(1.0394, brushSize);
+  // This needs to be clamped to prevent the delta from getting too large
+  const finalDelta = clamp(targetDelta, -20, 20);
+  // The new brush size is also clamped to prevent it from getting too large or small
+  const newBrushSize = clamp(brushSize + finalDelta, 1, 500);
+
+  return newBrushSize;
+};
+
 const useCanvasWheel = (stageRef: MutableRefObject<Konva.Stage | null>) => {
   const dispatch = useAppDispatch();
   const stageScale = useAppSelector((s) => s.canvas.stageScale);
@@ -36,15 +48,7 @@ const useCanvasWheel = (stageRef: MutableRefObject<Konva.Stage | null>) => {
       }
 
       if ($ctrl.get() || $meta.get()) {
-        // This equation was derived by fitting a curve to the desired brush sizes and deltas
-        // see https://github.com/invoke-ai/InvokeAI/pull/5542#issuecomment-1915847565
-        const targetDelta = Math.sign(delta) * 0.7363 * Math.pow(1.0394, brushSize);
-        // This needs to be clamped to prevent the delta from getting too large
-        const finalDelta = clamp(targetDelta, -20, 20);
-        // The new brush size is also clamped to prevent it from getting too large or small
-        const newBrushSize = clamp(brushSize + finalDelta, 1, 500);
-
-        dispatch(setBrushSize(newBrushSize));
+        dispatch(setBrushSize(calculateNewBrushSize(brushSize, delta)));
       } else {
         const cursorPos = stageRef.current.getPointerPosition();
         let delta = e.evt.deltaY;

invokeai/frontend/web/src/features/canvas/util/blobToDataURL.ts

@@ -7,3 +7,22 @@ export const blobToDataURL = (blob: Blob): Promise<string> => {
     reader.readAsDataURL(blob);
   });
 };
+
+export function imageDataToDataURL(imageData: ImageData): string {
+  const { width, height } = imageData;
+
+  // Create a canvas to transfer the ImageData to
+  const canvas = document.createElement('canvas');
+  canvas.width = width;
+  canvas.height = height;
+
+  // Draw the ImageData onto the canvas
+  const ctx = canvas.getContext('2d');
+  if (!ctx) {
+    throw new Error('Unable to get canvas context');
+  }
+  ctx.putImageData(imageData, 0, 0);
+
+  // Convert the canvas to a data URL (base64)
+  return canvas.toDataURL();
+}

invokeai/frontend/web/src/features/canvas/util/colorToString.ts

@@ -1,6 +1,11 @@
-import type { RgbaColor } from 'react-colorful';
+import type { RgbaColor, RgbColor } from 'react-colorful';
 
 export const rgbaColorToString = (color: RgbaColor): string => {
   const { r, g, b, a } = color;
   return `rgba(${r}, ${g}, ${b}, ${a})`;
 };
+
+export const rgbColorToString = (color: RgbColor): string => {
+  const { r, g, b } = color;
+  return `rgba(${r}, ${g}, ${b})`;
+};

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

@@ -21,6 +21,7 @@ import ControlAdapterShouldAutoConfig from './ControlAdapterShouldAutoConfig';
 import ControlNetCanvasImageImports from './imports/ControlNetCanvasImageImports';
 import { ParamControlAdapterBeginEnd } from './parameters/ParamControlAdapterBeginEnd';
 import ParamControlAdapterControlMode from './parameters/ParamControlAdapterControlMode';
+import ParamControlAdapterIPMethod from './parameters/ParamControlAdapterIPMethod';
 import ParamControlAdapterProcessorSelect from './parameters/ParamControlAdapterProcessorSelect';
 import ParamControlAdapterResizeMode from './parameters/ParamControlAdapterResizeMode';
 import ParamControlAdapterWeight from './parameters/ParamControlAdapterWeight';
@@ -111,7 +112,8 @@ const ControlAdapterConfig = (props: { id: string; number: number }) => {
 
       <Flex w="full" flexDir="column" gap={4}>
         <Flex gap={8} w="full" alignItems="center">
-          <Flex flexDir="column" gap={2} h={32} w="full">
+          <Flex flexDir="column" gap={4} h={controlAdapterType === 'ip_adapter' ? 40 : 32} w="full">
+            <ParamControlAdapterIPMethod id={id} />
             <ParamControlAdapterWeight id={id} />
             <ParamControlAdapterBeginEnd id={id} />
           </Flex>

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

@@ -0,0 +1,63 @@
+import type { ComboboxOnChange } from '@invoke-ai/ui-library';
+import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { useControlAdapterIPMethod } from 'features/controlAdapters/hooks/useControlAdapterIPMethod';
+import { useControlAdapterIsEnabled } from 'features/controlAdapters/hooks/useControlAdapterIsEnabled';
+import { controlAdapterIPMethodChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
+import type { IPMethod } from 'features/controlAdapters/store/types';
+import { isIPMethod } from 'features/controlAdapters/store/types';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+
+type Props = {
+  id: string;
+};
+
+const ParamControlAdapterIPMethod = ({ id }: Props) => {
+  const isEnabled = useControlAdapterIsEnabled(id);
+  const method = useControlAdapterIPMethod(id);
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const options: { label: string; value: IPMethod }[] = useMemo(
+    () => [
+      { label: t('controlnet.full'), value: 'full' },
+      { label: `${t('controlnet.style')} (${t('common.beta')})`, value: 'style' },
+      { label: `${t('controlnet.composition')} (${t('common.beta')})`, value: 'composition' },
+    ],
+    [t]
+  );
+
+  const handleIPMethodChanged = useCallback<ComboboxOnChange>(
+    (v) => {
+      if (!isIPMethod(v?.value)) {
+        return;
+      }
+      dispatch(
+        controlAdapterIPMethodChanged({
+          id,
+          method: v.value,
+        })
+      );
+    },
+    [id, dispatch]
+  );
+
+  const value = useMemo(() => options.find((o) => o.value === method), [options, method]);
+
+  if (!method) {
+    return null;
+  }
+
+  return (
+    <FormControl>
+      <InformationalPopover feature="ipAdapterMethod">
+        <FormLabel>{t('controlnet.ipAdapterMethod')}</FormLabel>
+      </InformationalPopover>
+      <Combobox value={value} options={options} isDisabled={!isEnabled} onChange={handleIPMethodChanged} />
+    </FormControl>
+  );
+};
+
+export default memo(ParamControlAdapterIPMethod);

invokeai/frontend/web/src/features/controlAdapters/hooks/useControlAdapterIPMethod.ts

@@ -0,0 +1,24 @@
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
+import { useAppSelector } from 'app/store/storeHooks';
+import {
+  selectControlAdapterById,
+  selectControlAdaptersSlice,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { useMemo } from 'react';
+
+export const useControlAdapterIPMethod = (id: string) => {
+  const selector = useMemo(
+    () =>
+      createMemoizedSelector(selectControlAdaptersSlice, (controlAdapters) => {
+        const cn = selectControlAdapterById(controlAdapters, id);
+        if (cn && cn?.type === 'ip_adapter') {
+          return cn.method;
+        }
+      }),
+    [id]
+  );
+
+  const method = useAppSelector(selector);
+
+  return method;
+};

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

@@ -6,6 +6,7 @@ import { deepClone } from 'common/util/deepClone';
 import { buildControlAdapter } from 'features/controlAdapters/util/buildControlAdapter';
 import { buildControlAdapterProcessor } from 'features/controlAdapters/util/buildControlAdapterProcessor';
 import { zModelIdentifierField } from 'features/nodes/types/common';
+import { maskLayerIPAdapterAdded } from 'features/regionalPrompts/store/regionalPromptsSlice';
 import { merge, uniq } from 'lodash-es';
 import type { ControlNetModelConfig, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
 import { socketInvocationError } from 'services/events/actions';
@@ -21,6 +22,7 @@ import type {
   ControlAdapterType,
   ControlMode,
   ControlNetConfig,
+  IPMethod,
   RequiredControlAdapterProcessorNode,
   ResizeMode,
   T2IAdapterConfig,
@@ -245,6 +247,10 @@ export const controlAdaptersSlice = createSlice({
       }
       caAdapter.updateOne(state, { id, changes: { controlMode } });
     },
+    controlAdapterIPMethodChanged: (state, action: PayloadAction<{ id: string; method: IPMethod }>) => {
+      const { id, method } = action.payload;
+      caAdapter.updateOne(state, { id, changes: { method } });
+    },
     controlAdapterCLIPVisionModelChanged: (
       state,
       action: PayloadAction<{ id: string; clipVisionModel: CLIPVisionModel }>
@@ -377,6 +383,10 @@ export const controlAdaptersSlice = createSlice({
     builder.addCase(socketInvocationError, (state) => {
       state.pendingControlImages = [];
     });
+
+    builder.addCase(maskLayerIPAdapterAdded, (state, action) => {
+      caAdapter.addOne(state, buildControlAdapter(action.meta.uuid, 'ip_adapter'));
+    });
   },
 });
 
@@ -390,6 +400,7 @@ export const {
   controlAdapterIsEnabledChanged,
   controlAdapterModelChanged,
   controlAdapterCLIPVisionModelChanged,
+  controlAdapterIPMethodChanged,
   controlAdapterWeightChanged,
   controlAdapterBeginStepPctChanged,
   controlAdapterEndStepPctChanged,

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

@@ -210,6 +210,10 @@ const zResizeMode = z.enum(['just_resize', 'crop_resize', 'fill_resize', 'just_r
 export type ResizeMode = z.infer<typeof zResizeMode>;
 export const isResizeMode = (v: unknown): v is ResizeMode => zResizeMode.safeParse(v).success;
 
+const zIPMethod = z.enum(['full', 'style', 'composition']);
+export type IPMethod = z.infer<typeof zIPMethod>;
+export const isIPMethod = (v: unknown): v is IPMethod => zIPMethod.safeParse(v).success;
+
 export type ControlNetConfig = {
   type: 'controlnet';
   id: string;
@@ -253,6 +257,7 @@ export type IPAdapterConfig = {
   model: ParameterIPAdapterModel | null;
   clipVisionModel: CLIPVisionModel;
   weight: number;
+  method: IPMethod;
   beginStepPct: number;
   endStepPct: number;
 };

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

@@ -46,6 +46,7 @@ export const initialIPAdapter: Omit<IPAdapterConfig, 'id'> = {
   isEnabled: true,
   controlImage: null,
   model: null,
+  method: 'full',
   clipVisionModel: 'ViT-H',
   weight: 1,
   beginStepPct: 0,

invokeai/frontend/web/src/features/gallery/components/Boards/BoardContextMenu.tsx

@@ -32,7 +32,7 @@ const BoardContextMenu = ({ board, board_id, setBoardToDelete, children }: Props
 
   const isSelectedForAutoAdd = useAppSelector(selectIsSelectedForAutoAdd);
   const boardName = useBoardName(board_id);
-  const isBulkDownloadEnabled = useFeatureStatus('bulkDownload').isFeatureEnabled;
+  const isBulkDownloadEnabled = useFeatureStatus('bulkDownload');
 
   const [bulkDownload] = useBulkDownloadImagesMutation();
 

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

@@ -54,7 +54,7 @@ const CurrentImageButtons = () => {
   const selection = useAppSelector((s) => s.gallery.selection);
   const shouldDisableToolbarButtons = useAppSelector(selectShouldDisableToolbarButtons);
 
-  const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
+  const isUpscalingEnabled = useFeatureStatus('upscaling');
   const isQueueMutationInProgress = useIsQueueMutationInProgress();
   const toaster = useAppToaster();
   const { t } = useTranslation();

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

@@ -20,7 +20,7 @@ const MultipleSelectionMenuItems = () => {
   const selection = useAppSelector((s) => s.gallery.selection);
   const customStarUi = useStore($customStarUI);
 
-  const isBulkDownloadEnabled = useFeatureStatus('bulkDownload').isFeatureEnabled;
+  const isBulkDownloadEnabled = useFeatureStatus('bulkDownload');
 
   const [starImages] = useStarImagesMutation();
   const [unstarImages] = useUnstarImagesMutation();

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

@@ -45,7 +45,7 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
   const toaster = useAppToaster();
-  const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
+  const isCanvasEnabled = useFeatureStatus('unifiedCanvas');
   const customStarUi = useStore($customStarUI);
   const { downloadImage } = useDownloadImage();
 

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

@@ -1,4 +1,4 @@
-import { Box, Flex, IconButton, Tooltip } from '@invoke-ai/ui-library';
+import { Box, Flex, IconButton, Tooltip, useShiftModifier } from '@invoke-ai/ui-library';
 import { getOverlayScrollbarsParams } from 'common/components/OverlayScrollbars/constants';
 import { isString } from 'lodash-es';
 import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
@@ -9,18 +9,19 @@ import { PiCopyBold, PiDownloadSimpleBold } from 'react-icons/pi';
 
 type Props = {
   label: string;
-  data: object | string;
+  data: unknown;
   fileName?: string;
   withDownload?: boolean;
   withCopy?: boolean;
+  extraCopyActions?: { label: string; getData: (data: unknown) => unknown }[];
 };
 
 const overlayscrollbarsOptions = getOverlayScrollbarsParams('scroll', 'scroll').options;
 
 const DataViewer = (props: Props) => {
-  const { label, data, fileName, withDownload = true, withCopy = true } = props;
+  const { label, data, fileName, withDownload = true, withCopy = true, extraCopyActions } = props;
   const dataString = useMemo(() => (isString(data) ? data : JSON.stringify(data, null, 2)), [data]);
-
+  const shift = useShiftModifier();
   const handleCopy = useCallback(() => {
     navigator.clipboard.writeText(dataString);
   }, [dataString]);
@@ -67,6 +68,10 @@ const DataViewer = (props: Props) => {
             />
           </Tooltip>
         )}
+        {shift &&
+          extraCopyActions?.map(({ label, getData }) => (
+            <ExtraCopyAction label={label} getData={getData} data={data} key={label} />
+          ))}
       </Flex>
     </Flex>
   );
@@ -78,3 +83,27 @@ const overlayScrollbarsStyles: CSSProperties = {
   height: '100%',
   width: '100%',
 };
+
+type ExtraCopyActionProps = {
+  label: string;
+  data: unknown;
+  getData: (data: unknown) => unknown;
+};
+const ExtraCopyAction = ({ label, data, getData }: ExtraCopyActionProps) => {
+  const { t } = useTranslation();
+  const handleCopy = useCallback(() => {
+    navigator.clipboard.writeText(JSON.stringify(getData(data), null, 2));
+  }, [data, getData]);
+
+  return (
+    <Tooltip label={`${t('gallery.copy')} ${label} JSON`}>
+      <IconButton
+        aria-label={`${t('gallery.copy')} ${label} JSON`}
+        icon={<PiCopyBold size={16} />}
+        variant="ghost"
+        opacity={0.7}
+        onClick={handleCopy}
+      />
+    </Tooltip>
+  );
+};

invokeai/frontend/web/src/features/gallery/hooks/useMultiselect.ts

@@ -18,7 +18,7 @@ export const useMultiselect = (imageDTO?: ImageDTO) => {
     [imageDTO?.image_name]
   );
   const isSelected = useAppSelector(selectIsSelected);
-  const isMultiSelectEnabled = useFeatureStatus('multiselect').isFeatureEnabled;
+  const isMultiSelectEnabled = useFeatureStatus('multiselect');
 
   const handleClick = useCallback(
     (e: MouseEvent<HTMLDivElement>) => {

invokeai/frontend/web/src/features/hrf/components/HrfSettings.tsx

@@ -8,7 +8,7 @@ import ParamHrfStrength from './ParamHrfStrength';
 import ParamHrfToggle from './ParamHrfToggle';
 
 export const HrfSettings = memo(() => {
-  const isHRFFeatureEnabled = useFeatureStatus('hrf').isFeatureEnabled;
+  const isHRFFeatureEnabled = useFeatureStatus('hrf');
   const hrfEnabled = useAppSelector((s) => s.hrf.hrfEnabled);
 
   if (!isHRFFeatureEnabled) {

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

@@ -386,6 +386,10 @@ const parseIPAdapter: MetadataParseFunc<IPAdapterConfigMetadata> = async (metada
     .nullish()
     .catch(null)
     .parse(await getProperty(metadataItem, 'weight'));
+  const method = zIPAdapterField.shape.method
+    .nullish()
+    .catch(null)
+    .parse(await getProperty(metadataItem, 'method'));
   const begin_step_percent = zIPAdapterField.shape.begin_step_percent
     .nullish()
     .catch(null)
@@ -403,6 +407,7 @@ const parseIPAdapter: MetadataParseFunc<IPAdapterConfigMetadata> = async (metada
     clipVisionModel: 'ViT-H',
     controlImage: image?.image_name ?? null,
     weight: weight ?? initialIPAdapter.weight,
+    method: method ?? initialIPAdapter.method,
     beginStepPct: begin_step_percent ?? initialIPAdapter.beginStepPct,
     endStepPct: end_step_percent ?? initialIPAdapter.endStepPct,
   };

invokeai/frontend/web/src/features/modelManagerV2/hooks/useStarterModelsToast.tsx

@@ -10,7 +10,7 @@ const TOAST_ID = 'starterModels';
 
 export const useStarterModelsToast = () => {
   const { t } = useTranslation();
-  const isEnabled = useFeatureStatus('starterModels').isFeatureEnabled;
+  const isEnabled = useFeatureStatus('starterModels');
   const [didToast, setDidToast] = useState(false);
   const [mainModels, { data }] = useMainModels();
   const toast = useToast();

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ControlNetOrT2IAdapterDefaultSettings/ControlNetOrT2IAdapterDefaultSettings.tsx

@@ -1,4 +1,4 @@
-import { Button, Flex, Heading, Text } from '@invoke-ai/ui-library';
+import { Button, Flex, Heading, SimpleGrid, Text } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { useControlNetOrT2IAdapterDefaultSettings } from 'features/modelManagerV2/hooks/useControlNetOrT2IAdapterDefaultSettings';
 import { DefaultPreprocessor } from 'features/modelManagerV2/subpanels/ModelPanel/ControlNetOrT2IAdapterDefaultSettings/DefaultPreprocessor';
@@ -92,13 +92,9 @@ export const ControlNetOrT2IAdapterDefaultSettings = () => {
         </Button>
       </Flex>
 
-      <Flex flexDir="column" gap={8}>
-        <Flex gap={8}>
-          <Flex gap={4} w="full">
-            <DefaultPreprocessor control={control} name="preprocessor" />
-          </Flex>
-        </Flex>
-      </Flex>
+      <SimpleGrid columns={2} gap={8}>
+        <DefaultPreprocessor control={control} name="preprocessor" />
+      </SimpleGrid>
     </>
   );
 };

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/MainModelDefaultSettings/MainModelDefaultSettings.tsx

@@ -1,4 +1,4 @@
-import { Button, Flex, Heading, Text } from '@invoke-ai/ui-library';
+import { Button, Flex, Heading, SimpleGrid, Text } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { useMainModelDefaultSettings } from 'features/modelManagerV2/hooks/useMainModelDefaultSettings';
 import { DefaultHeight } from 'features/modelManagerV2/subpanels/ModelPanel/MainModelDefaultSettings/DefaultHeight';
@@ -122,40 +122,16 @@ export const MainModelDefaultSettings = () => {
         </Button>
       </Flex>
 
-      <Flex flexDir="column" gap={8}>
-        <Flex gap={8}>
-          <Flex gap={4} w="full">
-            <DefaultVae control={control} name="vae" />
-          </Flex>
-          <Flex gap={4} w="full">
-            <DefaultVaePrecision control={control} name="vaePrecision" />
-          </Flex>
-        </Flex>
-        <Flex gap={8}>
-          <Flex gap={4} w="full">
-            <DefaultScheduler control={control} name="scheduler" />
-          </Flex>
-          <Flex gap={4} w="full">
-            <DefaultSteps control={control} name="steps" />
-          </Flex>
-        </Flex>
-        <Flex gap={8}>
-          <Flex gap={4} w="full">
-            <DefaultCfgScale control={control} name="cfgScale" />
-          </Flex>
-          <Flex gap={4} w="full">
-            <DefaultCfgRescaleMultiplier control={control} name="cfgRescaleMultiplier" />
-          </Flex>
-        </Flex>
-        <Flex gap={8}>
-          <Flex gap={4} w="full">
-            <DefaultWidth control={control} optimalDimension={optimalDimension} />
-          </Flex>
-          <Flex gap={4} w="full">
-            <DefaultHeight control={control} optimalDimension={optimalDimension} />
-          </Flex>
-        </Flex>
-      </Flex>
+      <SimpleGrid columns={2} gap={8}>
+        <DefaultVae control={control} name="vae" />
+        <DefaultVaePrecision control={control} name="vaePrecision" />
+        <DefaultScheduler control={control} name="scheduler" />
+        <DefaultSteps control={control} name="steps" />
+        <DefaultCfgScale control={control} name="cfgScale" />
+        <DefaultCfgRescaleMultiplier control={control} name="cfgRescaleMultiplier" />
+        <DefaultWidth control={control} optimalDimension={optimalDimension} />
+        <DefaultHeight control={control} optimalDimension={optimalDimension} />
+      </SimpleGrid>
     </>
   );
 };

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelEdit.tsx

@@ -6,6 +6,7 @@ import {
   FormLabel,
   Heading,
   Input,
+  SimpleGrid,
   Text,
   Textarea,
 } from '@invoke-ai/ui-library';
@@ -66,25 +67,21 @@ export const ModelEdit = ({ form }: Props) => {
           <Heading as="h3" fontSize="md" mt="4">
             {t('modelManager.modelSettings')}
           </Heading>
-          <Flex gap={4}>
+          <SimpleGrid columns={2} gap={4}>
             <FormControl flexDir="column" alignItems="flex-start" gap={1}>
               <FormLabel>{t('modelManager.baseModel')}</FormLabel>
               <BaseModelSelect control={form.control} />
             </FormControl>
-          </Flex>
-          {data.type === 'main' && data.format === 'checkpoint' && (
-            <>
-              <Flex gap={4}>
+            <FormControl flexDir="column" alignItems="flex-start" gap={1}>
+              <FormLabel>{t('modelManager.variant')}</FormLabel>
+              <ModelVariantSelect control={form.control} />
+            </FormControl>
+            {data.type === 'main' && data.format === 'checkpoint' && (
+              <>
                 <FormControl flexDir="column" alignItems="flex-start" gap={1}>
                   <FormLabel>{t('modelManager.pathToConfig')}</FormLabel>
                   <Input {...form.register('config_path', stringFieldOptions)} />
                 </FormControl>
-                <FormControl flexDir="column" alignItems="flex-start" gap={1}>
-                  <FormLabel>{t('modelManager.variant')}</FormLabel>
-                  <ModelVariantSelect control={form.control} />
-                </FormControl>
-              </Flex>
-              <Flex gap={4}>
                 <FormControl flexDir="column" alignItems="flex-start" gap={1}>
                   <FormLabel>{t('modelManager.predictionType')}</FormLabel>
                   <PredictionTypeSelect control={form.control} />
@@ -93,9 +90,9 @@ export const ModelEdit = ({ form }: Props) => {
                   <FormLabel>{t('modelManager.upcastAttention')}</FormLabel>
                   <Checkbox {...form.register('upcast_attention')} />
                 </FormControl>
-              </Flex>
-            </>
-          )}
+              </>
+            )}
+          </SimpleGrid>
         </Flex>
       </form>
     </Flex>

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/ModelView.tsx

@@ -1,4 +1,4 @@
-import { Box, Flex, Text } from '@invoke-ai/ui-library';
+import { Box, Flex, SimpleGrid, Text } from '@invoke-ai/ui-library';
 import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppSelector } from 'app/store/storeHooks';
 import { ControlNetOrT2IAdapterDefaultSettings } from 'features/modelManagerV2/subpanels/ModelPanel/ControlNetOrT2IAdapterDefaultSettings/ControlNetOrT2IAdapterDefaultSettings';
@@ -24,57 +24,32 @@ export const ModelView = () => {
   return (
     <Flex flexDir="column" h="full" gap={4}>
       <Box layerStyle="second" borderRadius="base" p={4}>
-        <Flex flexDir="column" gap={4}>
-          <Flex gap={2}>
-            <ModelAttrView label={t('modelManager.baseModel')} value={data.base} />
-            <ModelAttrView label={t('modelManager.modelType')} value={data.type} />
-          </Flex>
-          <Flex gap={2}>
-            <ModelAttrView label={t('common.format')} value={data.format} />
-            <ModelAttrView label={t('modelManager.path')} value={data.path} />
-          </Flex>
-
+        <SimpleGrid columns={2} gap={4}>
+          <ModelAttrView label={t('modelManager.baseModel')} value={data.base} />
+          <ModelAttrView label={t('modelManager.modelType')} value={data.type} />
+          <ModelAttrView label={t('common.format')} value={data.format} />
+          <ModelAttrView label={t('modelManager.path')} value={data.path} />
+          {data.type === 'main' && <ModelAttrView label={t('modelManager.variant')} value={data.variant} />}
           {data.type === 'main' && data.format === 'diffusers' && data.repo_variant && (
-            <Flex gap={2}>
-              <ModelAttrView label={t('modelManager.repoVariant')} value={data.repo_variant} />
-            </Flex>
+            <ModelAttrView label={t('modelManager.repoVariant')} value={data.repo_variant} />
           )}
-
           {data.type === 'main' && data.format === 'checkpoint' && (
             <>
-              <Flex gap={2}>
-                <ModelAttrView label={t('modelManager.pathToConfig')} value={data.config_path} />
-                <ModelAttrView label={t('modelManager.variant')} value={data.variant} />
-              </Flex>
-              <Flex gap={2}>
-                <ModelAttrView label={t('modelManager.predictionType')} value={data.prediction_type} />
-                <ModelAttrView label={t('modelManager.upcastAttention')} value={`${data.upcast_attention}`} />
-              </Flex>
+              <ModelAttrView label={t('modelManager.pathToConfig')} value={data.config_path} />
+              <ModelAttrView label={t('modelManager.predictionType')} value={data.prediction_type} />
+              <ModelAttrView label={t('modelManager.upcastAttention')} value={`${data.upcast_attention}`} />
             </>
           )}
-
           {data.type === 'ip_adapter' && data.format === 'invokeai' && (
-            <Flex gap={2}>
-              <ModelAttrView label={t('modelManager.imageEncoderModelId')} value={data.image_encoder_model_id} />
-            </Flex>
+            <ModelAttrView label={t('modelManager.imageEncoderModelId')} value={data.image_encoder_model_id} />
           )}
-        </Flex>
+        </SimpleGrid>
+      </Box>
+      <Box layerStyle="second" borderRadius="base" p={4}>
+        {data.type === 'main' && data.base !== 'sdxl-refiner' && <MainModelDefaultSettings />}
+        {(data.type === 'controlnet' || data.type === 't2i_adapter') && <ControlNetOrT2IAdapterDefaultSettings />}
+        {(data.type === 'main' || data.type === 'lora') && <TriggerPhrases />}
       </Box>
-      {data.type === 'main' && data.base !== 'sdxl-refiner' && (
-        <Box layerStyle="second" borderRadius="base" p={4}>
-          <MainModelDefaultSettings />
-        </Box>
-      )}
-      {(data.type === 'controlnet' || data.type === 't2i_adapter') && (
-        <Box layerStyle="second" borderRadius="base" p={4}>
-          <ControlNetOrT2IAdapterDefaultSettings />
-        </Box>
-      )}
-      {(data.type === 'main' || data.type === 'lora') && (
-        <Box layerStyle="second" borderRadius="base" p={4}>
-          <TriggerPhrases />
-        </Box>
-      )}
     </Flex>
   );
 };

invokeai/frontend/web/src/features/modelManagerV2/subpanels/ModelPanel/TriggerPhrases.tsx

@@ -77,9 +77,17 @@ export const TriggerPhrases = () => {
     [updateModel, selectedModelKey, triggerPhrases]
   );
 
+  const onTriggerPhraseAddFormSubmit = useCallback(
+    (e: React.FormEvent<HTMLFormElement>) => {
+      e.preventDefault();
+      addTriggerPhrase();
+    },
+    [addTriggerPhrase]
+  );
+
   return (
     <Flex flexDir="column" w="full" gap="5">
-      <form>
+      <form onSubmit={onTriggerPhraseAddFormSubmit}>
         <FormControl w="full" isInvalid={Boolean(errors.length)} orientation="vertical">
           <FormLabel>{t('modelManager.triggerPhrases')}</FormLabel>
           <Flex flexDir="column" w="full">

invokeai/frontend/web/src/features/nodes/components/flow/edges/InvocationDefaultEdge.tsx

@@ -1,8 +1,9 @@
+import { Flex, Text } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 import type { CSSProperties } from 'react';
 import { memo, useMemo } from 'react';
 import type { EdgeProps } from 'reactflow';
-import { BaseEdge, getBezierPath } from 'reactflow';
+import { BaseEdge, EdgeLabelRenderer, getBezierPath } from 'reactflow';
 
 import { makeEdgeSelector } from './util/makeEdgeSelector';
 
@@ -25,9 +26,10 @@ const InvocationDefaultEdge = ({
     [source, sourceHandleId, target, targetHandleId, selected]
   );
 
-  const { isSelected, shouldAnimate, stroke } = useAppSelector(selector);
+  const { isSelected, shouldAnimate, stroke, label } = useAppSelector(selector);
+  const shouldShowEdgeLabels = useAppSelector((s) => s.nodes.shouldShowEdgeLabels);
 
-  const [edgePath] = getBezierPath({
+  const [edgePath, labelX, labelY] = getBezierPath({
     sourceX,
     sourceY,
     sourcePosition,
@@ -47,7 +49,33 @@ const InvocationDefaultEdge = ({
     [isSelected, shouldAnimate, stroke]
   );
 
-  return <BaseEdge path={edgePath} markerEnd={markerEnd} style={edgeStyles} />;
+  return (
+    <>
+      <BaseEdge path={edgePath} markerEnd={markerEnd} style={edgeStyles} />
+      {label && shouldShowEdgeLabels && (
+        <EdgeLabelRenderer>
+          <Flex
+            className="nodrag nopan"
+            pointerEvents="all"
+            position="absolute"
+            transform={`translate(-50%, -50%) translate(${labelX}px,${labelY}px)`}
+            bg="base.800"
+            borderRadius="base"
+            borderWidth={1}
+            borderColor={isSelected ? 'undefined' : 'transparent'}
+            opacity={isSelected ? 1 : 0.5}
+            py={1}
+            px={3}
+            shadow="md"
+          >
+            <Text size="sm" fontWeight="semibold" color={isSelected ? 'base.100' : 'base.300'}>
+              {label}
+            </Text>
+          </Flex>
+        </EdgeLabelRenderer>
+      )}
+    </>
+  );
 };
 
 export default memo(InvocationDefaultEdge);

invokeai/frontend/web/src/features/nodes/components/flow/edges/util/makeEdgeSelector.ts

@@ -1,7 +1,7 @@
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { colorTokenToCssVar } from 'common/util/colorTokenToCssVar';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { selectFieldOutputTemplate } from 'features/nodes/store/selectors';
+import { selectFieldOutputTemplate, selectNodeTemplate } from 'features/nodes/store/selectors';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 
 import { getFieldColor } from './getEdgeColor';
@@ -10,6 +10,7 @@ const defaultReturnValue = {
   isSelected: false,
   shouldAnimate: false,
   stroke: colorTokenToCssVar('base.500'),
+  label: '',
 };
 
 export const makeEdgeSelector = (
@@ -19,25 +20,34 @@ export const makeEdgeSelector = (
   targetHandleId: string | null | undefined,
   selected?: boolean
 ) =>
-  createMemoizedSelector(selectNodesSlice, (nodes): { isSelected: boolean; shouldAnimate: boolean; stroke: string } => {
-    const sourceNode = nodes.nodes.find((node) => node.id === source);
-    const targetNode = nodes.nodes.find((node) => node.id === target);
+  createMemoizedSelector(
+    selectNodesSlice,
+    (nodes): { isSelected: boolean; shouldAnimate: boolean; stroke: string; label: string } => {
+      const sourceNode = nodes.nodes.find((node) => node.id === source);
+      const targetNode = nodes.nodes.find((node) => node.id === target);
 
-    const isInvocationToInvocationEdge = isInvocationNode(sourceNode) && isInvocationNode(targetNode);
+      const isInvocationToInvocationEdge = isInvocationNode(sourceNode) && isInvocationNode(targetNode);
 
-    const isSelected = Boolean(sourceNode?.selected || targetNode?.selected || selected);
-    if (!sourceNode || !sourceHandleId) {
-      return defaultReturnValue;
+      const isSelected = Boolean(sourceNode?.selected || targetNode?.selected || selected);
+      if (!sourceNode || !sourceHandleId || !targetNode || !targetHandleId) {
+        return defaultReturnValue;
+      }
+
+      const outputFieldTemplate = selectFieldOutputTemplate(nodes, sourceNode.id, sourceHandleId);
+      const sourceType = isInvocationToInvocationEdge ? outputFieldTemplate?.type : undefined;
+
+      const stroke = sourceType && nodes.shouldColorEdges ? getFieldColor(sourceType) : colorTokenToCssVar('base.500');
+
+      const sourceNodeTemplate = selectNodeTemplate(nodes, sourceNode.id);
+      const targetNodeTemplate = selectNodeTemplate(nodes, targetNode.id);
+
+      const label = `${sourceNodeTemplate?.title || sourceNode.data?.label} -> ${targetNodeTemplate?.title || targetNode.data?.label}`;
+
+      return {
+        isSelected,
+        shouldAnimate: nodes.shouldAnimateEdges && isSelected,
+        stroke,
+        label,
+      };
     }
-
-    const outputFieldTemplate = selectFieldOutputTemplate(nodes, sourceNode.id, sourceHandleId);
-    const sourceType = isInvocationToInvocationEdge ? outputFieldTemplate?.type : undefined;
-
-    const stroke = sourceType && nodes.shouldColorEdges ? getFieldColor(sourceType) : colorTokenToCssVar('base.500');
-
-    return {
-      isSelected,
-      shouldAnimate: nodes.shouldAnimateEdges && isSelected,
-      stroke,
-    };
-  });
+  );

invokeai/frontend/web/src/features/nodes/components/flow/nodes/Invocation/InvocationNodeFooter.tsx

@@ -16,7 +16,7 @@ const props: ChakraProps = { w: 'unset' };
 
 const InvocationNodeFooter = ({ nodeId }: Props) => {
   const hasImageOutput = useHasImageOutput(nodeId);
-  const isCacheEnabled = useFeatureStatus('invocationCache').isFeatureEnabled;
+  const isCacheEnabled = useFeatureStatus('invocationCache');
   return (
     <Flex
       className={DRAG_HANDLE_CLASSNAME}

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

@@ -24,6 +24,7 @@ import {
   selectNodesSlice,
   shouldAnimateEdgesChanged,
   shouldColorEdgesChanged,
+  shouldShowEdgeLabelsChanged,
   shouldSnapToGridChanged,
   shouldValidateGraphChanged,
 } from 'features/nodes/store/nodesSlice';
@@ -35,12 +36,20 @@ import { SelectionMode } from 'reactflow';
 const formLabelProps: FormLabelProps = { flexGrow: 1 };
 
 const selector = createMemoizedSelector(selectNodesSlice, (nodes) => {
-  const { shouldAnimateEdges, shouldValidateGraph, shouldSnapToGrid, shouldColorEdges, selectionMode } = nodes;
+  const {
+    shouldAnimateEdges,
+    shouldValidateGraph,
+    shouldSnapToGrid,
+    shouldColorEdges,
+    shouldShowEdgeLabels,
+    selectionMode,
+  } = nodes;
   return {
     shouldAnimateEdges,
     shouldValidateGraph,
     shouldSnapToGrid,
     shouldColorEdges,
+    shouldShowEdgeLabels,
     selectionModeIsChecked: selectionMode === SelectionMode.Full,
   };
 });
@@ -52,8 +61,14 @@ type Props = {
 const WorkflowEditorSettings = ({ children }: Props) => {
   const { isOpen, onOpen, onClose } = useDisclosure();
   const dispatch = useAppDispatch();
-  const { shouldAnimateEdges, shouldValidateGraph, shouldSnapToGrid, shouldColorEdges, selectionModeIsChecked } =
-    useAppSelector(selector);
+  const {
+    shouldAnimateEdges,
+    shouldValidateGraph,
+    shouldSnapToGrid,
+    shouldColorEdges,
+    shouldShowEdgeLabels,
+    selectionModeIsChecked,
+  } = useAppSelector(selector);
 
   const handleChangeShouldValidate = useCallback(
     (e: ChangeEvent<HTMLInputElement>) => {
@@ -90,6 +105,13 @@ const WorkflowEditorSettings = ({ children }: Props) => {
     [dispatch]
   );
 
+  const handleChangeShouldShowEdgeLabels = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      dispatch(shouldShowEdgeLabelsChanged(e.target.checked));
+    },
+    [dispatch]
+  );
+
   const { t } = useTranslation();
 
   return (
@@ -137,6 +159,14 @@ const WorkflowEditorSettings = ({ children }: Props) => {
                   <FormHelperText>{t('nodes.fullyContainNodesHelp')}</FormHelperText>
                 </FormControl>
                 <Divider />
+                <FormControl>
+                  <Flex w="full">
+                    <FormLabel>{t('nodes.showEdgeLabels')}</FormLabel>
+                    <Switch isChecked={shouldShowEdgeLabels} onChange={handleChangeShouldShowEdgeLabels} />
+                  </Flex>
+                  <FormHelperText>{t('nodes.showEdgeLabelsHelp')}</FormHelperText>
+                </FormControl>
+                <Divider />
                 <Heading size="sm" pt={4}>
                   {t('common.advanced')}
                 </Heading>

invokeai/frontend/web/src/features/nodes/hooks/useOutputFieldNames.ts

@@ -1,5 +1,5 @@
-import { createSelector } from '@reduxjs/toolkit';
 import { EMPTY_ARRAY } from 'app/store/constants';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
 import { selectNodeTemplate } from 'features/nodes/store/selectors';
@@ -10,7 +10,7 @@ import { useMemo } from 'react';
 export const useOutputFieldNames = (nodeId: string) => {
   const selector = useMemo(
     () =>
-      createSelector(selectNodesSlice, (nodes) => {
+      createMemoizedSelector(selectNodesSlice, (nodes) => {
         const template = selectNodeTemplate(nodes, nodeId);
         if (!template) {
           return EMPTY_ARRAY;

invokeai/frontend/web/src/features/nodes/hooks/useWithFooter.ts

@@ -5,8 +5,7 @@ import { useHasImageOutput } from './useHasImageOutput';
 
 export const useWithFooter = (nodeId: string) => {
   const hasImageOutput = useHasImageOutput(nodeId);
-  const isCacheEnabled = useFeatureStatus('invocationCache').isFeatureEnabled;
-
+  const isCacheEnabled = useFeatureStatus('invocationCache');
   const withFooter = useMemo(() => hasImageOutput || isCacheEnabled, [hasImageOutput, isCacheEnabled]);
   return withFooter;
 };

invokeai/frontend/web/src/features/nodes/store/nodesSlice.ts

@@ -103,6 +103,7 @@ const initialNodesState: NodesState = {
   shouldAnimateEdges: true,
   shouldSnapToGrid: false,
   shouldColorEdges: true,
+  shouldShowEdgeLabels: false,
   isAddNodePopoverOpen: false,
   nodeOpacity: 1,
   selectedNodes: [],
@@ -549,6 +550,9 @@ export const nodesSlice = createSlice({
     shouldAnimateEdgesChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldAnimateEdges = action.payload;
     },
+    shouldShowEdgeLabelsChanged: (state, action: PayloadAction<boolean>) => {
+      state.shouldShowEdgeLabels = action.payload;
+    },
     shouldSnapToGridChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldSnapToGrid = action.payload;
     },
@@ -831,6 +835,7 @@ export const {
   viewportChanged,
   edgeAdded,
   nodeTemplatesBuilt,
+  shouldShowEdgeLabelsChanged,
 } = nodesSlice.actions;
 
 // This is used for tracking `state.workflow.isTouched`

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

@@ -23,6 +23,7 @@ export type NodesState = {
   nodeOpacity: number;
   shouldSnapToGrid: boolean;
   shouldColorEdges: boolean;
+  shouldShowEdgeLabels: boolean;
   selectedNodes: string[];
   selectedEdges: string[];
   nodeExecutionStates: Record<string, NodeExecutionState>;

invokeai/frontend/web/src/features/nodes/types/common.ts

@@ -109,6 +109,7 @@ export const zIPAdapterField = z.object({
   image: zImageField,
   ip_adapter_model: zModelIdentifierField,
   weight: z.number(),
+  method: z.enum(['full', 'style', 'composition']),
   begin_step_percent: z.number().optional(),
   end_step_percent: z.number().optional(),
 });

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

@@ -19,12 +19,14 @@ export const addIPAdapterToLinearGraph = async (
   graph: NonNullableGraph,
   baseNodeId: string
 ): Promise<void> => {
-  const validIPAdapters = selectValidIPAdapters(state.controlAdapters).filter(({ model, controlImage, isEnabled }) => {
-    const hasModel = Boolean(model);
-    const doesBaseMatch = model?.base === state.generation.model?.base;
-    const hasControlImage = controlImage;
-    return isEnabled && hasModel && doesBaseMatch && hasControlImage;
-  });
+  const validIPAdapters = selectValidIPAdapters(state.controlAdapters)
+    .filter(({ model, controlImage, isEnabled }) => {
+      const hasModel = Boolean(model);
+      const doesBaseMatch = model?.base === state.generation.model?.base;
+      const hasControlImage = controlImage;
+      return isEnabled && hasModel && doesBaseMatch && hasControlImage;
+    })
+    .filter((ca) => !state.regionalPrompts.present.layers.some((l) => l.ipAdapterIds.includes(ca.id)));
 
   if (validIPAdapters.length) {
     // Even though denoise_latents' ip adapter input is collection or scalar, keep it simple and always use a collect
@@ -48,7 +50,7 @@ export const addIPAdapterToLinearGraph = async (
       if (!ipAdapter.model) {
         return;
       }
-      const { id, weight, model, clipVisionModel, beginStepPct, endStepPct, controlImage } = ipAdapter;
+      const { id, weight, model, clipVisionModel, method, beginStepPct, endStepPct, controlImage } = ipAdapter;
 
       assert(controlImage, 'IP Adapter image is required');
 
@@ -57,6 +59,7 @@ export const addIPAdapterToLinearGraph = async (
         type: 'ip_adapter',
         is_intermediate: true,
         weight: weight,
+        method: method,
         ip_adapter_model: model,
         clip_vision_model: clipVisionModel,
         begin_step_percent: beginStepPct,
@@ -84,7 +87,7 @@ export const addIPAdapterToLinearGraph = async (
 };
 
 const buildIPAdapterMetadata = (ipAdapter: IPAdapterConfig): S['IPAdapterMetadataField'] => {
-  const { controlImage, beginStepPct, endStepPct, model, clipVisionModel, weight } = ipAdapter;
+  const { controlImage, beginStepPct, endStepPct, model, clipVisionModel, method, weight } = ipAdapter;
 
   assert(model, 'IP Adapter model is required');
 
@@ -102,6 +105,7 @@ const buildIPAdapterMetadata = (ipAdapter: IPAdapterConfig): S['IPAdapterMetadat
     ip_adapter_model: model,
     clip_vision_model: clipVisionModel,
     weight,
+    method,
     begin_step_percent: beginStepPct,
     end_step_percent: endStepPct,
     image,

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

@@ -0,0 +1,346 @@
+import { getStore } from 'app/store/nanostores/store';
+import type { RootState } from 'app/store/store';
+import { selectAllIPAdapters } from 'features/controlAdapters/store/controlAdaptersSlice';
+import {
+  IP_ADAPTER_COLLECT,
+  NEGATIVE_CONDITIONING,
+  NEGATIVE_CONDITIONING_COLLECT,
+  POSITIVE_CONDITIONING,
+  POSITIVE_CONDITIONING_COLLECT,
+  PROMPT_REGION_INVERT_TENSOR_MASK_PREFIX,
+  PROMPT_REGION_MASK_TO_TENSOR_PREFIX,
+  PROMPT_REGION_NEGATIVE_COND_PREFIX,
+  PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX,
+  PROMPT_REGION_POSITIVE_COND_PREFIX,
+} from 'features/nodes/util/graph/constants';
+import { isVectorMaskLayer } from 'features/regionalPrompts/store/regionalPromptsSlice';
+import { getRegionalPromptLayerBlobs } from 'features/regionalPrompts/util/getLayerBlobs';
+import { size } from 'lodash-es';
+import { imagesApi } from 'services/api/endpoints/images';
+import type { CollectInvocation, Edge, IPAdapterInvocation, NonNullableGraph, S } from 'services/api/types';
+import { assert } from 'tsafe';
+
+export const addRegionalPromptsToGraph = async (state: RootState, graph: NonNullableGraph, denoiseNodeId: string) => {
+  if (!state.regionalPrompts.present.isEnabled) {
+    return;
+  }
+  const { dispatch } = getStore();
+  const isSDXL = state.generation.model?.base === 'sdxl';
+  const layers = state.regionalPrompts.present.layers
+    // Only support vector mask layers now
+    // TODO: Image masks
+    .filter(isVectorMaskLayer)
+    // Only visible layers are rendered on the canvas
+    .filter((l) => l.isVisible)
+    // Only layers with prompts get added to the graph
+    .filter((l) => {
+      const hasTextPrompt = Boolean(l.positivePrompt || l.negativePrompt);
+      const hasIPAdapter = l.ipAdapterIds.length !== 0;
+      return hasTextPrompt || hasIPAdapter;
+    });
+
+  const regionalIPAdapters = selectAllIPAdapters(state.controlAdapters).filter(
+    ({ id, model, controlImage, isEnabled }) => {
+      const hasModel = Boolean(model);
+      const doesBaseMatch = model?.base === state.generation.model?.base;
+      const hasControlImage = controlImage;
+      const isRegional = layers.some((l) => l.ipAdapterIds.includes(id));
+      return isEnabled && hasModel && doesBaseMatch && hasControlImage && isRegional;
+    }
+  );
+
+  const layerIds = layers.map((l) => l.id);
+  const blobs = await getRegionalPromptLayerBlobs(layerIds);
+  assert(size(blobs) === size(layerIds), 'Mismatch between layer IDs and blobs');
+
+  // TODO: We should probably just use conditioning collectors by default, and skip all this fanagling with re-routing
+  // the existing conditioning nodes.
+
+  // With regional prompts we have multiple conditioning nodes which much be routed into collectors. Set those up
+  const posCondCollectNode: CollectInvocation = {
+    id: POSITIVE_CONDITIONING_COLLECT,
+    type: 'collect',
+  };
+  graph.nodes[POSITIVE_CONDITIONING_COLLECT] = posCondCollectNode;
+  const negCondCollectNode: CollectInvocation = {
+    id: NEGATIVE_CONDITIONING_COLLECT,
+    type: 'collect',
+  };
+  graph.nodes[NEGATIVE_CONDITIONING_COLLECT] = negCondCollectNode;
+
+  // Re-route the denoise node's OG conditioning inputs to the collect nodes
+  const newEdges: Edge[] = [];
+  for (const edge of graph.edges) {
+    if (edge.destination.node_id === denoiseNodeId && edge.destination.field === 'positive_conditioning') {
+      newEdges.push({
+        source: edge.source,
+        destination: {
+          node_id: POSITIVE_CONDITIONING_COLLECT,
+          field: 'item',
+        },
+      });
+    } else if (edge.destination.node_id === denoiseNodeId && edge.destination.field === 'negative_conditioning') {
+      newEdges.push({
+        source: edge.source,
+        destination: {
+          node_id: NEGATIVE_CONDITIONING_COLLECT,
+          field: 'item',
+        },
+      });
+    } else {
+      newEdges.push(edge);
+    }
+  }
+  graph.edges = newEdges;
+
+  // Connect collectors to the denoise nodes - must happen _after_ rerouting else you get cycles
+  graph.edges.push({
+    source: {
+      node_id: POSITIVE_CONDITIONING_COLLECT,
+      field: 'collection',
+    },
+    destination: {
+      node_id: denoiseNodeId,
+      field: 'positive_conditioning',
+    },
+  });
+  graph.edges.push({
+    source: {
+      node_id: NEGATIVE_CONDITIONING_COLLECT,
+      field: 'collection',
+    },
+    destination: {
+      node_id: denoiseNodeId,
+      field: 'negative_conditioning',
+    },
+  });
+
+  if (!graph.nodes[IP_ADAPTER_COLLECT] && regionalIPAdapters.length > 0) {
+    const ipAdapterCollectNode: CollectInvocation = {
+      id: IP_ADAPTER_COLLECT,
+      type: 'collect',
+      is_intermediate: true,
+    };
+    graph.nodes[IP_ADAPTER_COLLECT] = ipAdapterCollectNode;
+    graph.edges.push({
+      source: { node_id: IP_ADAPTER_COLLECT, field: 'collection' },
+      destination: {
+        node_id: denoiseNodeId,
+        field: 'ip_adapter',
+      },
+    });
+  }
+
+  // Upload the blobs to the backend, add each to graph
+  // TODO: Store the uploaded image names in redux to reuse them, so long as the layer hasn't otherwise changed. This
+  // would be a great perf win - not only would we skip re-uploading the same image, but we'd be able to use the node
+  // cache (currently, when we re-use the same mask data, since it is a different image, the node cache is not used).
+  for (const layer of layers) {
+    const blob = blobs[layer.id];
+    assert(blob, `Blob for layer ${layer.id} not found`);
+
+    const file = new File([blob], `${layer.id}_mask.png`, { type: 'image/png' });
+    const req = dispatch(
+      imagesApi.endpoints.uploadImage.initiate({ file, image_category: 'mask', is_intermediate: true })
+    );
+    req.reset();
+
+    // TODO: This will raise on network error
+    const { image_name } = await req.unwrap();
+
+    // The main mask-to-tensor node
+    const maskToTensorNode: S['AlphaMaskToTensorInvocation'] = {
+      id: `${PROMPT_REGION_MASK_TO_TENSOR_PREFIX}_${layer.id}`,
+      type: 'alpha_mask_to_tensor',
+      image: {
+        image_name,
+      },
+    };
+    graph.nodes[maskToTensorNode.id] = maskToTensorNode;
+
+    if (layer.positivePrompt) {
+      // The main positive conditioning node
+      const regionalPositiveCondNode: S['SDXLCompelPromptInvocation'] | S['CompelInvocation'] = isSDXL
+        ? {
+            type: 'sdxl_compel_prompt',
+            id: `${PROMPT_REGION_POSITIVE_COND_PREFIX}_${layer.id}`,
+            prompt: layer.positivePrompt,
+            style: layer.positivePrompt, // TODO: Should we put the positive prompt in both fields?
+          }
+        : {
+            type: 'compel',
+            id: `${PROMPT_REGION_POSITIVE_COND_PREFIX}_${layer.id}`,
+            prompt: layer.positivePrompt,
+          };
+      graph.nodes[regionalPositiveCondNode.id] = regionalPositiveCondNode;
+
+      // Connect the mask to the conditioning
+      graph.edges.push({
+        source: { node_id: maskToTensorNode.id, field: 'mask' },
+        destination: { node_id: regionalPositiveCondNode.id, field: 'mask' },
+      });
+
+      // Connect the conditioning to the collector
+      graph.edges.push({
+        source: { node_id: regionalPositiveCondNode.id, field: 'conditioning' },
+        destination: { node_id: posCondCollectNode.id, field: 'item' },
+      });
+
+      // Copy the connections to the "global" positive conditioning node to the regional cond
+      for (const edge of graph.edges) {
+        if (edge.destination.node_id === POSITIVE_CONDITIONING && edge.destination.field !== 'prompt') {
+          graph.edges.push({
+            source: edge.source,
+            destination: { node_id: regionalPositiveCondNode.id, field: edge.destination.field },
+          });
+        }
+      }
+    }
+
+    if (layer.negativePrompt) {
+      // The main negative conditioning node
+      const regionalNegativeCondNode: S['SDXLCompelPromptInvocation'] | S['CompelInvocation'] = isSDXL
+        ? {
+            type: 'sdxl_compel_prompt',
+            id: `${PROMPT_REGION_NEGATIVE_COND_PREFIX}_${layer.id}`,
+            prompt: layer.negativePrompt,
+            style: layer.negativePrompt,
+          }
+        : {
+            type: 'compel',
+            id: `${PROMPT_REGION_NEGATIVE_COND_PREFIX}_${layer.id}`,
+            prompt: layer.negativePrompt,
+          };
+      graph.nodes[regionalNegativeCondNode.id] = regionalNegativeCondNode;
+
+      // Connect the mask to the conditioning
+      graph.edges.push({
+        source: { node_id: maskToTensorNode.id, field: 'mask' },
+        destination: { node_id: regionalNegativeCondNode.id, field: 'mask' },
+      });
+
+      // Connect the conditioning to the collector
+      graph.edges.push({
+        source: { node_id: regionalNegativeCondNode.id, field: 'conditioning' },
+        destination: { node_id: negCondCollectNode.id, field: 'item' },
+      });
+
+      // Copy the connections to the "global" negative conditioning node to the regional cond
+      for (const edge of graph.edges) {
+        if (edge.destination.node_id === NEGATIVE_CONDITIONING && edge.destination.field !== 'prompt') {
+          graph.edges.push({
+            source: edge.source,
+            destination: { node_id: regionalNegativeCondNode.id, field: edge.destination.field },
+          });
+        }
+      }
+    }
+
+    // If we are using the "invert" auto-negative setting, we need to add an additional negative conditioning node
+    if (layer.autoNegative === 'invert' && layer.positivePrompt) {
+      // We re-use the mask image, but invert it when converting to tensor
+      const invertTensorMaskNode: S['InvertTensorMaskInvocation'] = {
+        id: `${PROMPT_REGION_INVERT_TENSOR_MASK_PREFIX}_${layer.id}`,
+        type: 'invert_tensor_mask',
+      };
+      graph.nodes[invertTensorMaskNode.id] = invertTensorMaskNode;
+
+      // Connect the OG mask image to the inverted mask-to-tensor node
+      graph.edges.push({
+        source: {
+          node_id: maskToTensorNode.id,
+          field: 'mask',
+        },
+        destination: {
+          node_id: invertTensorMaskNode.id,
+          field: 'mask',
+        },
+      });
+
+      // Create the conditioning node. It's going to be connected to the negative cond collector, but it uses the
+      // positive prompt
+      const regionalPositiveCondInvertedNode: S['SDXLCompelPromptInvocation'] | S['CompelInvocation'] = isSDXL
+        ? {
+            type: 'sdxl_compel_prompt',
+            id: `${PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX}_${layer.id}`,
+            prompt: layer.positivePrompt,
+            style: layer.positivePrompt,
+          }
+        : {
+            type: 'compel',
+            id: `${PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX}_${layer.id}`,
+            prompt: layer.positivePrompt,
+          };
+      graph.nodes[regionalPositiveCondInvertedNode.id] = regionalPositiveCondInvertedNode;
+      // Connect the inverted mask to the conditioning
+      graph.edges.push({
+        source: { node_id: invertTensorMaskNode.id, field: 'mask' },
+        destination: { node_id: regionalPositiveCondInvertedNode.id, field: 'mask' },
+      });
+      // Connect the conditioning to the negative collector
+      graph.edges.push({
+        source: { node_id: regionalPositiveCondInvertedNode.id, field: 'conditioning' },
+        destination: { node_id: negCondCollectNode.id, field: 'item' },
+      });
+      // Copy the connections to the "global" positive conditioning node to our regional node
+      for (const edge of graph.edges) {
+        if (edge.destination.node_id === POSITIVE_CONDITIONING && edge.destination.field !== 'prompt') {
+          graph.edges.push({
+            source: edge.source,
+            destination: { node_id: regionalPositiveCondInvertedNode.id, field: edge.destination.field },
+          });
+        }
+      }
+    }
+
+    for (const ipAdapterId of layer.ipAdapterIds) {
+      const ipAdapter = selectAllIPAdapters(state.controlAdapters)
+        .filter(({ id, model, controlImage, isEnabled }) => {
+          const hasModel = Boolean(model);
+          const doesBaseMatch = model?.base === state.generation.model?.base;
+          const hasControlImage = controlImage;
+          const isRegional = layers.some((l) => l.ipAdapterIds.includes(id));
+          return isEnabled && hasModel && doesBaseMatch && hasControlImage && isRegional;
+        })
+        .find((ca) => ca.id === ipAdapterId);
+
+      if (!ipAdapter?.model) {
+        return;
+      }
+      const { id, weight, model, clipVisionModel, method, beginStepPct, endStepPct, controlImage } = ipAdapter;
+
+      assert(controlImage, 'IP Adapter image is required');
+
+      const ipAdapterNode: IPAdapterInvocation = {
+        id: `ip_adapter_${id}`,
+        type: 'ip_adapter',
+        is_intermediate: true,
+        weight: weight,
+        method: method,
+        ip_adapter_model: model,
+        clip_vision_model: clipVisionModel,
+        begin_step_percent: beginStepPct,
+        end_step_percent: endStepPct,
+        image: {
+          image_name: controlImage,
+        },
+      };
+
+      graph.nodes[ipAdapterNode.id] = ipAdapterNode;
+
+      // Connect the mask to the conditioning
+      graph.edges.push({
+        source: { node_id: maskToTensorNode.id, field: 'mask' },
+        destination: { node_id: ipAdapterNode.id, field: 'mask' },
+      });
+
+      graph.edges.push({
+        source: { node_id: ipAdapterNode.id, field: 'ip_adapter' },
+        destination: {
+          node_id: IP_ADAPTER_COLLECT,
+          field: 'item',
+        },
+      });
+    }
+  }
+};

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

@@ -9,6 +9,7 @@ import {
   CANVAS_TEXT_TO_IMAGE_GRAPH,
   IMAGE_TO_IMAGE_GRAPH,
   IMAGE_TO_LATENTS,
+  INPAINT_CREATE_MASK,
   INPAINT_IMAGE,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
@@ -145,6 +146,16 @@ export const addVAEToGraph = async (
           field: 'vae',
         },
       },
+      {
+        source: {
+          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          field: 'vae',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'vae',
+        },
+      },
 
       {
         source: {

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

@@ -133,6 +133,8 @@ export const buildCanvasInpaintGraph = async (
         coherence_mode: canvasCoherenceMode,
         minimum_denoise: canvasCoherenceMinDenoise,
         edge_radius: canvasCoherenceEdgeSize,
+        tiled: false,
+        fp32: fp32,
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -182,6 +184,16 @@ export const buildCanvasInpaintGraph = async (
           field: 'clip',
         },
       },
+      {
+        source: {
+          node_id: modelLoaderNodeId,
+          field: 'unet',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'unet',
+        },
+      },
       // Connect CLIP Skip to Conditioning
       {
         source: {
@@ -331,6 +343,16 @@ export const buildCanvasInpaintGraph = async (
           field: 'mask',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_IMAGE_RESIZE_UP,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Resize Down
       {
         source: {

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

@@ -157,6 +157,8 @@ export const buildCanvasOutpaintGraph = async (
         coherence_mode: canvasCoherenceMode,
         edge_radius: canvasCoherenceEdgeSize,
         minimum_denoise: canvasCoherenceMinDenoise,
+        tiled: false,
+        fp32: fp32,
       },
       [DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -207,6 +209,16 @@ export const buildCanvasOutpaintGraph = async (
           field: 'clip',
         },
       },
+      {
+        source: {
+          node_id: modelLoaderNodeId,
+          field: 'unet',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'unet',
+        },
+      },
       // Connect CLIP Skip to Conditioning
       {
         source: {
@@ -453,6 +465,16 @@ export const buildCanvasOutpaintGraph = async (
           field: 'image',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_IMAGE_RESIZE_UP,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Resize Results Down
       {
         source: {

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

@@ -135,6 +135,8 @@ export const buildCanvasSDXLInpaintGraph = async (
         coherence_mode: canvasCoherenceMode,
         minimum_denoise: refinerModel ? Math.max(0.2, canvasCoherenceMinDenoise) : canvasCoherenceMinDenoise,
         edge_radius: canvasCoherenceEdgeSize,
+        tiled: false,
+        fp32: fp32,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -214,6 +216,16 @@ export const buildCanvasSDXLInpaintGraph = async (
           field: 'clip2',
         },
       },
+      {
+        source: {
+          node_id: modelLoaderNodeId,
+          field: 'unet',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'unet',
+        },
+      },
       // Connect Everything To Inpaint Node
       {
         source: {
@@ -342,6 +354,16 @@ export const buildCanvasSDXLInpaintGraph = async (
           field: 'mask',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_IMAGE_RESIZE_UP,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Resize Down
       {
         source: {

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

@@ -157,6 +157,8 @@ export const buildCanvasSDXLOutpaintGraph = async (
         coherence_mode: canvasCoherenceMode,
         edge_radius: canvasCoherenceEdgeSize,
         minimum_denoise: refinerModel ? Math.max(0.2, canvasCoherenceMinDenoise) : canvasCoherenceMinDenoise,
+        tiled: false,
+        fp32: fp32,
       },
       [SDXL_DENOISE_LATENTS]: {
         type: 'denoise_latents',
@@ -237,6 +239,16 @@ export const buildCanvasSDXLOutpaintGraph = async (
           field: 'clip2',
         },
       },
+      {
+        source: {
+          node_id: modelLoaderNodeId,
+          field: 'unet',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'unet',
+        },
+      },
       // Connect Infill Result To Inpaint Image
       {
         source: {
@@ -451,6 +463,16 @@ export const buildCanvasSDXLOutpaintGraph = async (
           field: 'image',
         },
       },
+      {
+        source: {
+          node_id: INPAINT_IMAGE_RESIZE_UP,
+          field: 'image',
+        },
+        destination: {
+          node_id: INPAINT_CREATE_MASK,
+          field: 'image',
+        },
+      },
       // Take combined mask and resize
       {
         source: {

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

@@ -1,6 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
 import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import { addRegionalPromptsToGraph } from 'features/nodes/util/graph/addRegionalPromptsToGraph';
 import { isNonRefinerMainModelConfig, type NonNullableGraph } from 'services/api/types';
 
 import { addControlNetToLinearGraph } from './addControlNetToLinearGraph';
@@ -273,6 +274,8 @@ export const buildLinearSDXLTextToImageGraph = async (state: RootState): Promise
 
   await addT2IAdaptersToLinearGraph(state, graph, SDXL_DENOISE_LATENTS);
 
+  await addRegionalPromptsToGraph(state, graph, SDXL_DENOISE_LATENTS);
+
   // NSFW & watermark - must be last thing added to graph
   if (state.system.shouldUseNSFWChecker) {
     // must add before watermarker!

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

@@ -1,6 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { RootState } from 'app/store/store';
 import { fetchModelConfigWithTypeGuard } from 'features/metadata/util/modelFetchingHelpers';
+import { addRegionalPromptsToGraph } from 'features/nodes/util/graph/addRegionalPromptsToGraph';
 import { getBoardField, getIsIntermediate } from 'features/nodes/util/graph/graphBuilderUtils';
 import { isNonRefinerMainModelConfig, type NonNullableGraph } from 'services/api/types';
 
@@ -255,6 +256,8 @@ export const buildLinearTextToImageGraph = async (state: RootState): Promise<Non
 
   await addT2IAdaptersToLinearGraph(state, graph, DENOISE_LATENTS);
 
+  await addRegionalPromptsToGraph(state, graph, DENOISE_LATENTS);
+
   // High resolution fix.
   if (state.hrf.hrfEnabled) {
     addHrfToGraph(state, graph);

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

@@ -46,6 +46,13 @@ export const SDXL_REFINER_DENOISE_LATENTS = 'sdxl_refiner_denoise_latents';
 export const SDXL_REFINER_INPAINT_CREATE_MASK = 'refiner_inpaint_create_mask';
 export const SEAMLESS = 'seamless';
 export const SDXL_REFINER_SEAMLESS = 'refiner_seamless';
+export const PROMPT_REGION_MASK_TO_TENSOR_PREFIX = 'prompt_region_mask_to_tensor';
+export const PROMPT_REGION_INVERT_TENSOR_MASK_PREFIX = 'prompt_region_invert_tensor_mask';
+export const PROMPT_REGION_POSITIVE_COND_PREFIX = 'prompt_region_positive_cond';
+export const PROMPT_REGION_NEGATIVE_COND_PREFIX = 'prompt_region_negative_cond';
+export const PROMPT_REGION_POSITIVE_COND_INVERTED_PREFIX = 'prompt_region_positive_cond_inverted';
+export const POSITIVE_CONDITIONING_COLLECT = 'positive_conditioning_collect';
+export const NEGATIVE_CONDITIONING_COLLECT = 'negative_conditioning_collect';
 
 // friendly graph ids
 export const TEXT_TO_IMAGE_GRAPH = 'text_to_image_graph';

invokeai/frontend/web/src/features/parameters/components/Canvas/InfillAndScaling/ParamInfillColorOptions.tsx

@@ -1,24 +1,18 @@
 import { Box, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
+import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIColorPicker from 'common/components/IAIColorPicker';
 import { selectGenerationSlice, setInfillColorValue } from 'features/parameters/store/generationSlice';
-import { memo, useCallback, useMemo } from 'react';
+import { memo, useCallback } from 'react';
 import type { RgbaColor } from 'react-colorful';
 import { useTranslation } from 'react-i18next';
 
+const selectInfillColor = createMemoizedSelector(selectGenerationSlice, (generation) => generation.infillColorValue);
+
 const ParamInfillColorOptions = () => {
   const dispatch = useAppDispatch();
 
-  const selector = useMemo(
-    () =>
-      createSelector(selectGenerationSlice, (generation) => ({
-        infillColor: generation.infillColorValue,
-      })),
-    []
-  );
-
-  const { infillColor } = useAppSelector(selector);
+  const infillColor = useAppSelector(selectInfillColor);
 
   const infillMethod = useAppSelector((s) => s.generation.infillMethod);
 

invokeai/frontend/web/src/features/parameters/components/Canvas/InfillAndScaling/ParamInfillMosaicOptions.tsx

@@ -1,35 +1,23 @@
 import { Box, CompositeNumberInput, CompositeSlider, Flex, FormControl, FormLabel } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIColorPicker from 'common/components/IAIColorPicker';
 import {
-  selectGenerationSlice,
   setInfillMosaicMaxColor,
   setInfillMosaicMinColor,
   setInfillMosaicTileHeight,
   setInfillMosaicTileWidth,
 } from 'features/parameters/store/generationSlice';
-import { memo, useCallback, useMemo } from 'react';
+import { memo, useCallback } from 'react';
 import type { RgbaColor } from 'react-colorful';
 import { useTranslation } from 'react-i18next';
 
 const ParamInfillMosaicTileSize = () => {
   const dispatch = useAppDispatch();
 
-  const selector = useMemo(
-    () =>
-      createSelector(selectGenerationSlice, (generation) => ({
-        infillMosaicTileWidth: generation.infillMosaicTileWidth,
-        infillMosaicTileHeight: generation.infillMosaicTileHeight,
-        infillMosaicMinColor: generation.infillMosaicMinColor,
-        infillMosaicMaxColor: generation.infillMosaicMaxColor,
-      })),
-    []
-  );
-
-  const { infillMosaicTileWidth, infillMosaicTileHeight, infillMosaicMinColor, infillMosaicMaxColor } =
-    useAppSelector(selector);
-
+  const infillMosaicTileWidth = useAppSelector((s) => s.generation.infillMosaicTileWidth);
+  const infillMosaicTileHeight = useAppSelector((s) => s.generation.infillMosaicTileHeight);
+  const infillMosaicMinColor = useAppSelector((s) => s.generation.infillMosaicMinColor);
+  const infillMosaicMaxColor = useAppSelector((s) => s.generation.infillMosaicMaxColor);
   const infillMethod = useAppSelector((s) => s.generation.infillMethod);
 
   const { t } = useTranslation();

invokeai/frontend/web/src/features/parameters/components/ImageSize/AspectRatioCanvasPreview.tsx

@@ -0,0 +1,13 @@
+import { Flex } from '@invoke-ai/ui-library';
+import { StageComponent } from 'features/regionalPrompts/components/StageComponent';
+import { memo } from 'react';
+
+export const AspectRatioCanvasPreview = memo(() => {
+  return (
+    <Flex w="full" h="full" alignItems="center" justifyContent="center" position="relative">
+      <StageComponent asPreview />
+    </Flex>
+  );
+});
+
+AspectRatioCanvasPreview.displayName = 'AspectRatioCanvasPreview';

invokeai/frontend/web/src/features/parameters/components/ImageSize/AspectRatioIconPreview.tsx

@@ -2,7 +2,7 @@ import { useSize } from '@chakra-ui/react-use-size';
 import { Flex, Icon } from '@invoke-ai/ui-library';
 import { useImageSizeContext } from 'features/parameters/components/ImageSize/ImageSizeContext';
 import { AnimatePresence, motion } from 'framer-motion';
-import { useMemo, useRef } from 'react';
+import { memo, useMemo, useRef } from 'react';
 import { PiFrameCorners } from 'react-icons/pi';
 
 import {
@@ -15,7 +15,7 @@ import {
   MOTION_ICON_INITIAL,
 } from './constants';
 
-export const AspectRatioPreview = () => {
+export const AspectRatioIconPreview = memo(() => {
   const ctx = useImageSizeContext();
   const containerRef = useRef<HTMLDivElement>(null);
   const containerSize = useSize(containerRef);
@@ -70,4 +70,6 @@ export const AspectRatioPreview = () => {
       </Flex>
     </Flex>
   );
-};
+});
+
+AspectRatioIconPreview.displayName = 'AspectRatioIconPreview';

invokeai/frontend/web/src/features/parameters/components/ImageSize/ImageSize.tsx

@@ -1,6 +1,5 @@
 import type { FormLabelProps } from '@invoke-ai/ui-library';
 import { Flex, FormControlGroup } from '@invoke-ai/ui-library';
-import { AspectRatioPreview } from 'features/parameters/components/ImageSize/AspectRatioPreview';
 import { AspectRatioSelect } from 'features/parameters/components/ImageSize/AspectRatioSelect';
 import type { ImageSizeContextInnerValue } from 'features/parameters/components/ImageSize/ImageSizeContext';
 import { ImageSizeContext } from 'features/parameters/components/ImageSize/ImageSizeContext';
@@ -13,10 +12,11 @@ import { memo } from 'react';
 type ImageSizeProps = ImageSizeContextInnerValue & {
   widthComponent: ReactNode;
   heightComponent: ReactNode;
+  previewComponent: ReactNode;
 };
 
 export const ImageSize = memo((props: ImageSizeProps) => {
-  const { widthComponent, heightComponent, ...ctx } = props;
+  const { widthComponent, heightComponent, previewComponent, ...ctx } = props;
   return (
     <ImageSizeContext.Provider value={ctx}>
       <Flex gap={4} alignItems="center">
@@ -33,7 +33,7 @@ export const ImageSize = memo((props: ImageSizeProps) => {
           </FormControlGroup>
         </Flex>
         <Flex w="108px" h="108px" flexShrink={0} flexGrow={0}>
-          <AspectRatioPreview />
+          {previewComponent}
         </Flex>
       </Flex>
     </ImageSizeContext.Provider>

invokeai/frontend/web/src/features/parameters/components/ImageSize/constants.ts

@@ -1,7 +1,6 @@
 import type { ComboboxOption } from '@invoke-ai/ui-library';
 
 import type { AspectRatioID, AspectRatioState } from './types';
-
 // When the aspect ratio is between these two values, we show the icon (experimentally determined)
 export const ICON_LOW_CUTOFF = 0.23;
 export const ICON_HIGH_CUTOFF = 1 / ICON_LOW_CUTOFF;
@@ -25,7 +24,6 @@ export const ICON_CONTAINER_STYLES = {
   alignItems: 'center',
   justifyContent: 'center',
 };
-
 export const ASPECT_RATIO_OPTIONS: ComboboxOption[] = [
   { label: 'Free' as const, value: 'Free' },
   { label: '16:9' as const, value: '16:9' },