fix(ui): excessive toasts when generating on canvas

- Add `withToast` flag to `uploadImage` util
- Skip the toast if this is not set
- Use the flag to disable toasts when canvas does internal image-uploading stuff that should be invisible to user

.github/pull_request_template.md

@@ -19,3 +19,4 @@
 - [ ] _The PR has a short but descriptive title, suitable for a changelog_
 - [ ] _Tests added / updated (if applicable)_
 - [ ] _Documentation added / updated (if applicable)_
+- [ ] _Updated `What's New` copy (if doing a release after this PR)_

invokeai/app/api/routers/app_info.py

@@ -40,6 +40,8 @@ class AppVersion(BaseModel):
 
     version: str = Field(description="App version")
 
+    highlights: Optional[list[str]] = Field(default=None, description="Highlights of release")
+
 
 class AppDependencyVersions(BaseModel):
     """App depencency Versions Response"""

invokeai/app/invocations/fields.py

@@ -41,6 +41,7 @@ class UIType(str, Enum, metaclass=MetaEnum):
     # region Model Field Types
     MainModel = "MainModelField"
     FluxMainModel = "FluxMainModelField"
+    SD3MainModel = "SD3MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
@@ -52,6 +53,8 @@ class UIType(str, Enum, metaclass=MetaEnum):
     T2IAdapterModel = "T2IAdapterModelField"
     T5EncoderModel = "T5EncoderModelField"
     CLIPEmbedModel = "CLIPEmbedModelField"
+    CLIPLEmbedModel = "CLIPLEmbedModelField"
+    CLIPGEmbedModel = "CLIPGEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -131,8 +134,10 @@ class FieldDescriptions:
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
     t5_encoder = "T5 tokenizer and text encoder"
     clip_embed_model = "CLIP Embed loader"
+    clip_g_model = "CLIP-G Embed loader"
     unet = "UNet (scheduler, LoRAs)"
     transformer = "Transformer"
+    mmditx = "MMDiTX"
     vae = "VAE"
     cond = "Conditioning tensor"
     controlnet_model = "ControlNet model to load"
@@ -140,6 +145,7 @@ class FieldDescriptions:
     lora_model = "LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
     flux_model = "Flux model (Transformer) to load"
+    sd3_model = "SD3 model (MMDiTX) to load"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
@@ -246,6 +252,12 @@ class FluxConditioningField(BaseModel):
     conditioning_name: str = Field(description="The name of conditioning tensor")
 
 
+class SD3ConditioningField(BaseModel):
+    """A conditioning tensor primitive value"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+
+
 class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/flux_denoise.py

@@ -56,7 +56,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.2.0",
+    version="3.2.1",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -81,6 +81,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         description=FieldDescriptions.denoising_start,
     )
     denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
+    add_noise: bool = InputField(default=True, description="Add noise based on denoising start.")
     transformer: TransformerField = InputField(
         description=FieldDescriptions.flux_model,
         input=Input.Connection,
@@ -207,9 +208,12 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                     "to be poor. Consider using a FLUX dev model instead."
                 )
 
-            # Noise the orig_latents by the appropriate amount for the first timestep.
-            t_0 = timesteps[0]
-            x = t_0 * noise + (1.0 - t_0) * init_latents
+            if self.add_noise:
+                # Noise the orig_latents by the appropriate amount for the first timestep.
+                t_0 = timesteps[0]
+                x = t_0 * noise + (1.0 - t_0) * init_latents
+            else:
+                x = init_latents
         else:
             # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
             if self.denoising_start > 1e-5:

invokeai/app/invocations/flux_model_loader.py

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

invokeai/app/invocations/model.py

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

invokeai/app/invocations/primitives.py

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

invokeai/app/invocations/sd3_denoise.py

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

invokeai/app/invocations/sd3_latents_to_image.py

@@ -0,0 +1,73 @@
+from contextlib import nullcontext
+
+import torch
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from einops import rearrange
+from PIL import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "sd3_l2i",
+    title="SD3 Latents to Image",
+    tags=["latents", "image", "vae", "l2i", "sd3"],
+    category="latents",
+    version="1.3.0",
+)
+class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Generates an image from latents."""
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+
+        vae_info = context.models.load(self.vae.vae)
+        assert isinstance(vae_info.model, (AutoencoderKL))
+        with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
+            assert isinstance(vae, (AutoencoderKL))
+            latents = latents.to(vae.device)
+
+            vae.disable_tiling()
+
+            tiling_context = nullcontext()
+
+            # clear memory as vae decode can request a lot
+            TorchDevice.empty_cache()
+
+            with torch.inference_mode(), tiling_context:
+                # copied from diffusers pipeline
+                latents = latents / vae.config.scaling_factor
+                img = vae.decode(latents, return_dict=False)[0]
+
+            img = img.clamp(-1, 1)
+            img = rearrange(img[0], "c h w -> h w c")  # noqa: F821
+            img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
+
+        TorchDevice.empty_cache()
+
+        image_dto = context.images.save(image=img_pil)
+
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/sd3_model_loader.py

@@ -0,0 +1,108 @@
+from typing import Optional
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import SubModelType
+
+
+@invocation_output("sd3_model_loader_output")
+class Sd3ModelLoaderOutput(BaseInvocationOutput):
+    """SD3 base model loader output."""
+
+    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
+    clip_l: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP L")
+    clip_g: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP G")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+
+
+@invocation(
+    "sd3_model_loader",
+    title="SD3 Main Model",
+    tags=["model", "sd3"],
+    category="model",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class Sd3ModelLoaderInvocation(BaseInvocation):
+    """Loads a SD3 base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.sd3_model,
+        ui_type=UIType.SD3MainModel,
+        input=Input.Direct,
+    )
+
+    t5_encoder_model: Optional[ModelIdentifierField] = InputField(
+        description=FieldDescriptions.t5_encoder,
+        ui_type=UIType.T5EncoderModel,
+        input=Input.Direct,
+        title="T5 Encoder",
+        default=None,
+    )
+
+    clip_l_model: Optional[ModelIdentifierField] = InputField(
+        description=FieldDescriptions.clip_embed_model,
+        ui_type=UIType.CLIPLEmbedModel,
+        input=Input.Direct,
+        title="CLIP L Encoder",
+        default=None,
+    )
+
+    clip_g_model: Optional[ModelIdentifierField] = InputField(
+        description=FieldDescriptions.clip_g_model,
+        ui_type=UIType.CLIPGEmbedModel,
+        input=Input.Direct,
+        title="CLIP G Encoder",
+        default=None,
+    )
+
+    vae_model: Optional[ModelIdentifierField] = InputField(
+        description=FieldDescriptions.vae_model, ui_type=UIType.VAEModel, title="VAE", default=None
+    )
+
+    def invoke(self, context: InvocationContext) -> Sd3ModelLoaderOutput:
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        vae = (
+            self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
+            if self.vae_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+        )
+        tokenizer_l = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        clip_encoder_l = (
+            self.clip_l_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+            if self.clip_l_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        )
+        tokenizer_g = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        clip_encoder_g = (
+            self.clip_g_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+            if self.clip_g_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
+        )
+        tokenizer_t5 = (
+            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
+            if self.t5_encoder_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
+        )
+        t5_encoder = (
+            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
+            if self.t5_encoder_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
+        )
+
+        return Sd3ModelLoaderOutput(
+            transformer=TransformerField(transformer=transformer, loras=[]),
+            clip_l=CLIPField(tokenizer=tokenizer_l, text_encoder=clip_encoder_l, loras=[], skipped_layers=0),
+            clip_g=CLIPField(tokenizer=tokenizer_g, text_encoder=clip_encoder_g, loras=[], skipped_layers=0),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer_t5, text_encoder=t5_encoder),
+            vae=VAEField(vae=vae),
+        )

invokeai/app/invocations/sd3_text_encoder.py

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

invokeai/app/invocations/segment_anything.py

@@ -5,7 +5,7 @@ from typing import Literal
 import numpy as np
 import torch
 from PIL import Image
-from pydantic import BaseModel, Field, model_validator
+from pydantic import BaseModel, Field
 from transformers import AutoModelForMaskGeneration, AutoProcessor
 from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
@@ -77,19 +77,14 @@ class SegmentAnythingInvocation(BaseInvocation):
         default="all",
     )
 
-    @model_validator(mode="after")
-    def check_point_lists_or_bounding_box(self):
-        if self.point_lists is None and self.bounding_boxes is None:
-            raise ValueError("Either point_lists or bounding_box must be provided.")
-        elif self.point_lists is not None and self.bounding_boxes is not None:
-            raise ValueError("Only one of point_lists or bounding_box can be provided.")
-        return self
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> MaskOutput:
         # The models expect a 3-channel RGB image.
         image_pil = context.images.get_pil(self.image.image_name, mode="RGB")
 
+        if self.point_lists is not None and self.bounding_boxes is not None:
+            raise ValueError("Only one of point_lists or bounding_box can be provided.")
+
         if (not self.bounding_boxes or len(self.bounding_boxes) == 0) and (
             not self.point_lists or len(self.point_lists) == 0
         ):

invokeai/app/services/model_records/model_records_base.py

@@ -15,6 +15,7 @@ from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
+    ClipVariantType,
     ControlAdapterDefaultSettings,
     MainModelDefaultSettings,
     ModelFormat,
@@ -85,7 +86,7 @@ class ModelRecordChanges(BaseModelExcludeNull):
 
     # Checkpoint-specific changes
     # TODO(MM2): Should we expose these? Feels footgun-y...
-    variant: Optional[ModelVariantType] = Field(description="The variant of the model.", default=None)
+    variant: Optional[ModelVariantType | ClipVariantType] = Field(description="The variant of the model.", default=None)
     prediction_type: Optional[SchedulerPredictionType] = Field(
         description="The prediction type of the model.", default=None
     )

invokeai/app/services/workflow_records/default_workflows/SD3.5 Text to Image.json

@@ -0,0 +1,382 @@
+{
+    "name": "SD3.5 Text to Image",
+   "author": "InvokeAI",
+    "description": "Sample text to image workflow for Stable Diffusion 3.5",
+    "version": "1.0.0",
+    "contact": "invoke@invoke.ai",
+    "tags": "text2image, SD3.5, default",
+  "notes": "",
+    "exposedFields": [
+      {
+        "nodeId": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "fieldName": "model"
+      },
+      {
+        "nodeId": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "fieldName": "prompt"
+      }
+    ],
+    "meta": {
+      "version": "3.0.0",
+      "category": "default"
+    },
+    "id": "e3a51d6b-8208-4d6d-b187-fcfe8b32934c",
+    "nodes": [
+      {
+        "id": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "type": "invocation",
+        "data": {
+          "id": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+          "type": "sd3_model_loader",
+          "version": "1.0.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "model": {
+              "name": "model",
+              "label": "",
+              "value": {
+                "key": "f7b20be9-92a8-4cfb-bca4-6c3b5535c10b",
+                "hash": "placeholder",
+                "name": "stable-diffusion-3.5-medium",
+                "base": "sd-3",
+                "type": "main"
+              }
+            },
+            "t5_encoder_model": {
+              "name": "t5_encoder_model",
+              "label": ""
+            },
+            "clip_l_model": {
+              "name": "clip_l_model",
+              "label": ""
+            },
+            "clip_g_model": {
+              "name": "clip_g_model",
+              "label": ""
+            },
+            "vae_model": {
+              "name": "vae_model",
+              "label": ""
+            }
+          }
+        },
+        "position": {
+          "x": -55.58689609637031,
+          "y": -111.53602444662268
+        }
+      },
+      {
+        "id": "f7e394ac-6394-4096-abcb-de0d346506b3",
+        "type": "invocation",
+        "data": {
+          "id": "f7e394ac-6394-4096-abcb-de0d346506b3",
+          "type": "rand_int",
+          "version": "1.0.1",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": false,
+          "nodePack": "invokeai",
+          "inputs": {
+            "low": {
+              "name": "low",
+              "label": "",
+              "value": 0
+            },
+            "high": {
+              "name": "high",
+              "label": "",
+              "value": 2147483647
+            }
+          }
+        },
+        "position": {
+          "x": 470.45870147220353,
+          "y": 350.3141781644303
+        }
+      },
+      {
+        "id": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
+        "type": "invocation",
+        "data": {
+          "id": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
+          "type": "sd3_l2i",
+          "version": "1.3.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": false,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "board": {
+              "name": "board",
+              "label": ""
+            },
+            "metadata": {
+              "name": "metadata",
+              "label": ""
+            },
+            "latents": {
+              "name": "latents",
+              "label": ""
+            },
+            "vae": {
+              "name": "vae",
+              "label": ""
+            }
+          }
+        },
+        "position": {
+          "x": 1192.3097009334897,
+          "y": -366.0994675072209
+        }
+      },
+      {
+        "id": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "type": "invocation",
+        "data": {
+          "id": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+          "type": "sd3_text_encoder",
+          "version": "1.0.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "clip_l": {
+              "name": "clip_l",
+              "label": ""
+            },
+            "clip_g": {
+              "name": "clip_g",
+              "label": ""
+            },
+            "t5_encoder": {
+              "name": "t5_encoder",
+              "label": ""
+            },
+            "prompt": {
+              "name": "prompt",
+              "label": "",
+              "value": ""
+            }
+          }
+        },
+        "position": {
+          "x": 408.16054647924784,
+          "y": 65.06415352118786
+        }
+      },
+      {
+        "id": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "type": "invocation",
+        "data": {
+          "id": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+          "type": "sd3_text_encoder",
+          "version": "1.0.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "clip_l": {
+              "name": "clip_l",
+              "label": ""
+            },
+            "clip_g": {
+              "name": "clip_g",
+              "label": ""
+            },
+            "t5_encoder": {
+              "name": "t5_encoder",
+              "label": ""
+            },
+            "prompt": {
+              "name": "prompt",
+              "label": "",
+              "value": ""
+            }
+          }
+        },
+        "position": {
+          "x": 378.9283412440941,
+          "y": -302.65777497352553
+        }
+      },
+      {
+        "id": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "type": "invocation",
+        "data": {
+          "id": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+          "type": "sd3_denoise",
+          "version": "1.0.0",
+          "label": "",
+          "notes": "",
+          "isOpen": true,
+          "isIntermediate": true,
+          "useCache": true,
+          "nodePack": "invokeai",
+          "inputs": {
+            "board": {
+              "name": "board",
+              "label": ""
+            },
+            "metadata": {
+              "name": "metadata",
+              "label": ""
+            },
+            "transformer": {
+              "name": "transformer",
+              "label": ""
+            },
+            "positive_conditioning": {
+              "name": "positive_conditioning",
+              "label": ""
+            },
+            "negative_conditioning": {
+              "name": "negative_conditioning",
+              "label": ""
+            },
+            "cfg_scale": {
+              "name": "cfg_scale",
+              "label": "",
+              "value": 3.5
+            },
+            "width": {
+              "name": "width",
+              "label": "",
+              "value": 1024
+            },
+            "height": {
+              "name": "height",
+              "label": "",
+              "value": 1024
+            },
+            "steps": {
+              "name": "steps",
+              "label": "",
+              "value": 30
+            },
+            "seed": {
+              "name": "seed",
+              "label": "",
+              "value": 0
+            }
+          }
+        },
+        "position": {
+          "x": 813.7814762740603,
+          "y": -142.20529727605867
+        }
+      }
+    ],
+    "edges": [
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cvae-9eb72af0-dd9e-4ec5-ad87-d65e3c01f48bvae",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
+        "sourceHandle": "vae",
+        "targetHandle": "vae"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ct5_encoder-3b4f7f27-cfc0-4373-a009-99c5290d0cd6t5_encoder",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "sourceHandle": "t5_encoder",
+        "targetHandle": "t5_encoder"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ct5_encoder-e17d34e7-6ed1-493c-9a85-4fcd291cb084t5_encoder",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "sourceHandle": "t5_encoder",
+        "targetHandle": "t5_encoder"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_g-3b4f7f27-cfc0-4373-a009-99c5290d0cd6clip_g",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "sourceHandle": "clip_g",
+        "targetHandle": "clip_g"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_g-e17d34e7-6ed1-493c-9a85-4fcd291cb084clip_g",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "sourceHandle": "clip_g",
+        "targetHandle": "clip_g"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_l-3b4f7f27-cfc0-4373-a009-99c5290d0cd6clip_l",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "sourceHandle": "clip_l",
+        "targetHandle": "clip_l"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4cclip_l-e17d34e7-6ed1-493c-9a85-4fcd291cb084clip_l",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "sourceHandle": "clip_l",
+        "targetHandle": "clip_l"
+      },
+      {
+        "id": "reactflow__edge-3f22f668-0e02-4fde-a2bb-c339586ceb4ctransformer-c7539f7b-7ac5-49b9-93eb-87ede611409ftransformer",
+        "type": "default",
+        "source": "3f22f668-0e02-4fde-a2bb-c339586ceb4c",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "transformer",
+        "targetHandle": "transformer"
+      },
+      {
+        "id": "reactflow__edge-f7e394ac-6394-4096-abcb-de0d346506b3value-c7539f7b-7ac5-49b9-93eb-87ede611409fseed",
+        "type": "default",
+        "source": "f7e394ac-6394-4096-abcb-de0d346506b3",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "value",
+        "targetHandle": "seed"
+      },
+      {
+        "id": "reactflow__edge-c7539f7b-7ac5-49b9-93eb-87ede611409flatents-9eb72af0-dd9e-4ec5-ad87-d65e3c01f48blatents",
+        "type": "default",
+        "source": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "target": "9eb72af0-dd9e-4ec5-ad87-d65e3c01f48b",
+        "sourceHandle": "latents",
+        "targetHandle": "latents"
+      },
+      {
+        "id": "reactflow__edge-e17d34e7-6ed1-493c-9a85-4fcd291cb084conditioning-c7539f7b-7ac5-49b9-93eb-87ede611409fpositive_conditioning",
+        "type": "default",
+        "source": "e17d34e7-6ed1-493c-9a85-4fcd291cb084",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "conditioning",
+        "targetHandle": "positive_conditioning"
+      },
+      {
+        "id": "reactflow__edge-3b4f7f27-cfc0-4373-a009-99c5290d0cd6conditioning-c7539f7b-7ac5-49b9-93eb-87ede611409fnegative_conditioning",
+        "type": "default",
+        "source": "3b4f7f27-cfc0-4373-a009-99c5290d0cd6",
+        "target": "c7539f7b-7ac5-49b9-93eb-87ede611409f",
+        "sourceHandle": "conditioning",
+        "targetHandle": "negative_conditioning"
+      }
+    ]
+  }

invokeai/app/util/step_callback.py

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

invokeai/backend/model_manager/config.py

@@ -53,6 +53,7 @@ class BaseModelType(str, Enum):
     Any = "any"
     StableDiffusion1 = "sd-1"
     StableDiffusion2 = "sd-2"
+    StableDiffusion3 = "sd-3"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
     Flux = "flux"
@@ -83,8 +84,10 @@ class SubModelType(str, Enum):
     Transformer = "transformer"
     TextEncoder = "text_encoder"
     TextEncoder2 = "text_encoder_2"
+    TextEncoder3 = "text_encoder_3"
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
+    Tokenizer3 = "tokenizer_3"
     VAE = "vae"
     VAEDecoder = "vae_decoder"
     VAEEncoder = "vae_encoder"
@@ -92,6 +95,13 @@ class SubModelType(str, Enum):
     SafetyChecker = "safety_checker"
 
 
+class ClipVariantType(str, Enum):
+    """Variant type."""
+
+    L = "large"
+    G = "gigantic"
+
+
 class ModelVariantType(str, Enum):
     """Variant type."""
 
@@ -147,6 +157,17 @@ class ModelSourceType(str, Enum):
 DEFAULTS_PRECISION = Literal["fp16", "fp32"]
 
 
+AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]
+
+
+class SubmodelDefinition(BaseModel):
+    path_or_prefix: str
+    model_type: ModelType
+    variant: AnyVariant = None
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
 class MainModelDefaultSettings(BaseModel):
     vae: str | None = Field(default=None, description="Default VAE for this model (model key)")
     vae_precision: DEFAULTS_PRECISION | None = Field(default=None, description="Default VAE precision for this model")
@@ -193,6 +214,9 @@ class ModelConfigBase(BaseModel):
         schema["required"].extend(["key", "type", "format"])
 
     model_config = ConfigDict(validate_assignment=True, json_schema_extra=json_schema_extra)
+    submodels: Optional[Dict[SubModelType, SubmodelDefinition]] = Field(
+        description="Loadable submodels in this model", default=None
+    )
 
 
 class CheckpointConfigBase(ModelConfigBase):
@@ -335,7 +359,7 @@ class MainConfigBase(ModelConfigBase):
     default_settings: Optional[MainModelDefaultSettings] = Field(
         description="Default settings for this model", default=None
     )
-    variant: ModelVariantType = ModelVariantType.Normal
+    variant: AnyVariant = ModelVariantType.Normal
 
 
 class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
@@ -419,12 +443,33 @@ class CLIPEmbedDiffusersConfig(DiffusersConfigBase):
 
     type: Literal[ModelType.CLIPEmbed] = ModelType.CLIPEmbed
     format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
+    variant: ClipVariantType = ClipVariantType.L
 
     @staticmethod
     def get_tag() -> Tag:
         return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}")
 
 
+class CLIPGEmbedDiffusersConfig(CLIPEmbedDiffusersConfig):
+    """Model config for CLIP-G Embeddings."""
+
+    variant: ClipVariantType = ClipVariantType.G
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}.{ClipVariantType.G}")
+
+
+class CLIPLEmbedDiffusersConfig(CLIPEmbedDiffusersConfig):
+    """Model config for CLIP-L Embeddings."""
+
+    variant: ClipVariantType = ClipVariantType.L
+
+    @staticmethod
+    def get_tag() -> Tag:
+        return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}.{ClipVariantType.L}")
+
+
 class CLIPVisionDiffusersConfig(DiffusersConfigBase):
     """Model config for CLIPVision."""
 
@@ -501,6 +546,8 @@ AnyModelConfig = Annotated[
         Annotated[SpandrelImageToImageConfig, SpandrelImageToImageConfig.get_tag()],
         Annotated[CLIPVisionDiffusersConfig, CLIPVisionDiffusersConfig.get_tag()],
         Annotated[CLIPEmbedDiffusersConfig, CLIPEmbedDiffusersConfig.get_tag()],
+        Annotated[CLIPLEmbedDiffusersConfig, CLIPLEmbedDiffusersConfig.get_tag()],
+        Annotated[CLIPGEmbedDiffusersConfig, CLIPGEmbedDiffusersConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]

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

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

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

@@ -42,6 +42,7 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 @ModelLoaderRegistry.register(
     base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Diffusers
 )
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion3, type=ModelType.Main, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Checkpoint)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Checkpoint)
 @ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Checkpoint)
@@ -51,13 +52,6 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 class StableDiffusionDiffusersModel(GenericDiffusersLoader):
     """Class to load main models."""
 
-    model_base_to_model_type = {
-        BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",
-        BaseModelType.StableDiffusion2: "FrozenOpenCLIPEmbedder",
-        BaseModelType.StableDiffusionXL: "SDXL",
-        BaseModelType.StableDiffusionXLRefiner: "SDXL-Refiner",
-    }
-
     def _load_model(
         self,
         config: AnyModelConfig,

invokeai/backend/model_manager/probe.py

@@ -1,7 +1,7 @@
 import json
 import re
 from pathlib import Path
-from typing import Any, Dict, Literal, Optional, Union
+from typing import Any, Callable, Dict, Literal, Optional, Union
 
 import safetensors.torch
 import spandrel
@@ -22,6 +22,7 @@ from invokeai.backend.lora.conversions.flux_kohya_lora_conversion_utils import i
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
+    AnyVariant,
     BaseModelType,
     ControlAdapterDefaultSettings,
     InvalidModelConfigException,
@@ -33,8 +34,15 @@ from invokeai.backend.model_manager.config import (
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
+    SubmodelDefinition,
+    SubModelType,
+)
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import ConfigLoader
+from invokeai.backend.model_manager.util.model_util import (
+    get_clip_variant_type,
+    lora_token_vector_length,
+    read_checkpoint_meta,
 )
-from invokeai.backend.model_manager.util.model_util import lora_token_vector_length, read_checkpoint_meta
 from invokeai.backend.quantization.gguf.ggml_tensor import GGMLTensor
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
@@ -112,6 +120,7 @@ class ModelProbe(object):
         "StableDiffusionXLPipeline": ModelType.Main,
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
         "StableDiffusionXLInpaintPipeline": ModelType.Main,
+        "StableDiffusion3Pipeline": ModelType.Main,
         "LatentConsistencyModelPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.VAE,
         "AutoencoderTiny": ModelType.VAE,
@@ -122,8 +131,12 @@ class ModelProbe(object):
         "CLIPTextModel": ModelType.CLIPEmbed,
         "T5EncoderModel": ModelType.T5Encoder,
         "FluxControlNetModel": ModelType.ControlNet,
+        "SD3Transformer2DModel": ModelType.Main,
+        "CLIPTextModelWithProjection": ModelType.CLIPEmbed,
     }
 
+    TYPE2VARIANT: Dict[ModelType, Callable[[str], Optional[AnyVariant]]] = {ModelType.CLIPEmbed: get_clip_variant_type}
+
     @classmethod
     def register_probe(
         cls, format: Literal["diffusers", "checkpoint", "onnx"], model_type: ModelType, probe_class: type[ProbeBase]
@@ -170,7 +183,10 @@ class ModelProbe(object):
         fields["path"] = model_path.as_posix()
         fields["type"] = fields.get("type") or model_type
         fields["base"] = fields.get("base") or probe.get_base_type()
-        fields["variant"] = fields.get("variant") or probe.get_variant_type()
+        variant_func = cls.TYPE2VARIANT.get(fields["type"], None)
+        fields["variant"] = (
+            fields.get("variant") or (variant_func and variant_func(model_path.as_posix())) or probe.get_variant_type()
+        )
         fields["prediction_type"] = fields.get("prediction_type") or probe.get_scheduler_prediction_type()
         fields["image_encoder_model_id"] = fields.get("image_encoder_model_id") or probe.get_image_encoder_model_id()
         fields["name"] = fields.get("name") or cls.get_model_name(model_path)
@@ -217,6 +233,10 @@ class ModelProbe(object):
                 and fields["prediction_type"] == SchedulerPredictionType.VPrediction
             )
 
+        get_submodels = getattr(probe, "get_submodels", None)
+        if fields["base"] == BaseModelType.StableDiffusion3 and callable(get_submodels):
+            fields["submodels"] = get_submodels()
+
         model_info = ModelConfigFactory.make_config(fields)  # , key=fields.get("key", None))
         return model_info
 
@@ -747,18 +767,33 @@ class FolderProbeBase(ProbeBase):
 
 class PipelineFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
-        with open(self.model_path / "unet" / "config.json", "r") as file:
-            unet_conf = json.load(file)
-        if unet_conf["cross_attention_dim"] == 768:
-            return BaseModelType.StableDiffusion1
-        elif unet_conf["cross_attention_dim"] == 1024:
-            return BaseModelType.StableDiffusion2
-        elif unet_conf["cross_attention_dim"] == 1280:
-            return BaseModelType.StableDiffusionXLRefiner
-        elif unet_conf["cross_attention_dim"] == 2048:
-            return BaseModelType.StableDiffusionXL
-        else:
-            raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+        # Handle pipelines with a UNet (i.e SD 1.x, SD2, SDXL).
+        config_path = self.model_path / "unet" / "config.json"
+        if config_path.exists():
+            with open(config_path) as file:
+                unet_conf = json.load(file)
+            if unet_conf["cross_attention_dim"] == 768:
+                return BaseModelType.StableDiffusion1
+            elif unet_conf["cross_attention_dim"] == 1024:
+                return BaseModelType.StableDiffusion2
+            elif unet_conf["cross_attention_dim"] == 1280:
+                return BaseModelType.StableDiffusionXLRefiner
+            elif unet_conf["cross_attention_dim"] == 2048:
+                return BaseModelType.StableDiffusionXL
+            else:
+                raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+
+        # Handle pipelines with a transformer (i.e. SD3).
+        config_path = self.model_path / "transformer" / "config.json"
+        if config_path.exists():
+            with open(config_path) as file:
+                transformer_conf = json.load(file)
+            if transformer_conf["_class_name"] == "SD3Transformer2DModel":
+                return BaseModelType.StableDiffusion3
+            else:
+                raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
+
+        raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
 
     def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
         with open(self.model_path / "scheduler" / "scheduler_config.json", "r") as file:
@@ -770,6 +805,23 @@ class PipelineFolderProbe(FolderProbeBase):
         else:
             raise InvalidModelConfigException("Unknown scheduler prediction type: {scheduler_conf['prediction_type']}")
 
+    def get_submodels(self) -> Dict[SubModelType, SubmodelDefinition]:
+        config = ConfigLoader.load_config(self.model_path, config_name="model_index.json")
+        submodels: Dict[SubModelType, SubmodelDefinition] = {}
+        for key, value in config.items():
+            if key.startswith("_") or not (isinstance(value, list) and len(value) == 2):
+                continue
+            model_loader = str(value[1])
+            if model_type := ModelProbe.CLASS2TYPE.get(model_loader):
+                variant_func = ModelProbe.TYPE2VARIANT.get(model_type, None)
+                submodels[SubModelType(key)] = SubmodelDefinition(
+                    path_or_prefix=(self.model_path / key).resolve().as_posix(),
+                    model_type=model_type,
+                    variant=variant_func and variant_func((self.model_path / key).as_posix()),
+                )
+
+        return submodels
+
     def get_variant_type(self) -> ModelVariantType:
         # This only works for pipelines! Any kind of
         # exception results in our returning the

invokeai/backend/model_manager/starter_models.py

@@ -140,6 +140,22 @@ flux_dev = StarterModel(
     type=ModelType.Main,
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
+sd35_medium = StarterModel(
+    name="SD3.5 Medium",
+    base=BaseModelType.StableDiffusion3,
+    source="stabilityai/stable-diffusion-3.5-medium",
+    description="Medium SD3.5 Model: ~15GB",
+    type=ModelType.Main,
+    dependencies=[],
+)
+sd35_large = StarterModel(
+    name="SD3.5 Large",
+    base=BaseModelType.StableDiffusion3,
+    source="stabilityai/stable-diffusion-3.5-large",
+    description="Large SD3.5 Model: ~19G",
+    type=ModelType.Main,
+    dependencies=[],
+)
 cyberrealistic_sd1 = StarterModel(
     name="CyberRealistic v4.1",
     base=BaseModelType.StableDiffusion1,
@@ -570,6 +586,8 @@ STARTER_MODELS: list[StarterModel] = [
     flux_dev_quantized,
     flux_schnell,
     flux_dev,
+    sd35_medium,
+    sd35_large,
     cyberrealistic_sd1,
     rev_animated_sd1,
     dreamshaper_8_sd1,

invokeai/backend/model_manager/util/model_util.py

@@ -8,6 +8,7 @@ import safetensors
 import torch
 from picklescan.scanner import scan_file_path
 
+from invokeai.backend.model_manager.config import ClipVariantType
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 
 
@@ -165,3 +166,25 @@ def convert_bundle_to_flux_transformer_checkpoint(
         del transformer_state_dict[k]
 
     return original_state_dict
+
+
+def get_clip_variant_type(location: str) -> Optional[ClipVariantType]:
+    try:
+        path = Path(location)
+        config_path = path / "config.json"
+        if not config_path.exists():
+            config_path = path / "text_encoder" / "config.json"
+        if not config_path.exists():
+            return ClipVariantType.L
+        with open(config_path) as file:
+            clip_conf = json.load(file)
+            hidden_size = clip_conf.get("hidden_size", -1)
+            match hidden_size:
+                case 1280:
+                    return ClipVariantType.G
+                case 768:
+                    return ClipVariantType.L
+                case _:
+                    return ClipVariantType.L
+    except Exception:
+        return ClipVariantType.L

invokeai/backend/model_manager/util/select_hf_files.py

@@ -85,6 +85,7 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
     """Select the proper variant files from a list of HuggingFace repo_id paths."""
     result: set[Path] = set()
     subfolder_weights: dict[Path, list[SubfolderCandidate]] = {}
+    safetensors_detected = False
     for path in files:
         if path.suffix in [".onnx", ".pb", ".onnx_data"]:
             if variant == ModelRepoVariant.ONNX:
@@ -119,19 +120,27 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
             # We prefer safetensors over other file formats and an exact variant match. We'll score each file based on
             # variant and format and select the best one.
 
+            if safetensors_detected and path.suffix == ".bin":
+                continue
+
             parent = path.parent
             score = 0
 
             if path.suffix == ".safetensors":
+                safetensors_detected = True
+                if parent in subfolder_weights:
+                    subfolder_weights[parent] = [sfc for sfc in subfolder_weights[parent] if sfc.path.suffix != ".bin"]
                 score += 1
 
             candidate_variant_label = path.suffixes[0] if len(path.suffixes) == 2 else None
 
             # Some special handling is needed here if there is not an exact match and if we cannot infer the variant
             # from the file name. In this case, we only give this file a point if the requested variant is FP32 or DEFAULT.
-            if candidate_variant_label == f".{variant}" or (
-                not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.Default]
-            ):
+            if (
+                variant is not ModelRepoVariant.Default
+                and candidate_variant_label
+                and candidate_variant_label.startswith(f".{variant.value}")
+            ) or (not candidate_variant_label and variant in [ModelRepoVariant.FP32, ModelRepoVariant.Default]):
                 score += 1
 
             if parent not in subfolder_weights:
@@ -146,7 +155,7 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
         # Check if at least one of the files has the explicit fp16 variant.
         at_least_one_fp16 = False
         for candidate in candidate_list:
-            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0] == ".fp16":
+            if len(candidate.path.suffixes) == 2 and candidate.path.suffixes[0].startswith(".fp16"):
                 at_least_one_fp16 = True
                 break
 
@@ -162,7 +171,16 @@ def _filter_by_variant(files: List[Path], variant: ModelRepoVariant) -> Set[Path
         # candidate.
         highest_score_candidate = max(candidate_list, key=lambda candidate: candidate.score)
         if highest_score_candidate:
-            result.add(highest_score_candidate.path)
+            pattern = r"^(.*?)-\d+-of-\d+(\.\w+)$"
+            match = re.match(pattern, highest_score_candidate.path.as_posix())
+            if match:
+                for candidate in candidate_list:
+                    if candidate.path.as_posix().startswith(match.group(1)) and candidate.path.as_posix().endswith(
+                        match.group(2)
+                    ):
+                        result.add(candidate.path)
+            else:
+                result.add(highest_score_candidate.path)
 
     # If one of the architecture-related variants was specified and no files matched other than
     # config and text files then we return an empty list

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

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

invokeai/frontend/web/knip.ts

@@ -9,6 +9,7 @@ const config: KnipConfig = {
     'src/services/api/schema.ts',
     'src/features/nodes/types/v1/**',
     'src/features/nodes/types/v2/**',
+    'src/features/parameters/types/parameterSchemas.ts',
     // TODO(psyche): maybe we can clean up these utils after canvas v2 release
     'src/features/controlLayers/konva/util.ts',
     // TODO(psyche): restore HRF functionality?

invokeai/frontend/web/package.json

@@ -52,11 +52,11 @@
     }
   },
   "dependencies": {
+    "@atlaskit/pragmatic-drag-and-drop": "^1.4.0",
+    "@atlaskit/pragmatic-drag-and-drop-auto-scroll": "^1.4.0",
+    "@atlaskit/pragmatic-drag-and-drop-hitbox": "^1.0.3",
     "@dagrejs/dagre": "^1.1.4",
     "@dagrejs/graphlib": "^2.2.4",
-    "@dnd-kit/core": "^6.1.0",
-    "@dnd-kit/sortable": "^8.0.0",
-    "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.1.0",
     "@invoke-ai/ui-library": "^0.0.43",
     "@nanostores/react": "^0.7.3",

invokeai/frontend/web/pnpm-lock.yaml

@@ -5,21 +5,21 @@ settings:
   excludeLinksFromLockfile: false
 
 dependencies:
+  '@atlaskit/pragmatic-drag-and-drop':
+    specifier: ^1.4.0
+    version: 1.4.0
+  '@atlaskit/pragmatic-drag-and-drop-auto-scroll':
+    specifier: ^1.4.0
+    version: 1.4.0
+  '@atlaskit/pragmatic-drag-and-drop-hitbox':
+    specifier: ^1.0.3
+    version: 1.0.3
   '@dagrejs/dagre':
     specifier: ^1.1.4
     version: 1.1.4
   '@dagrejs/graphlib':
     specifier: ^2.2.4
     version: 2.2.4
-  '@dnd-kit/core':
-    specifier: ^6.1.0
-    version: 6.1.0(react-dom@18.3.1)(react@18.3.1)
-  '@dnd-kit/sortable':
-    specifier: ^8.0.0
-    version: 8.0.0(@dnd-kit/core@6.1.0)(react@18.3.1)
-  '@dnd-kit/utilities':
-    specifier: ^3.2.2
-    version: 3.2.2(react@18.3.1)
   '@fontsource-variable/inter':
     specifier: ^5.1.0
     version: 5.1.0
@@ -319,6 +319,28 @@ packages:
       '@jridgewell/trace-mapping': 0.3.25
     dev: true
 
+  /@atlaskit/pragmatic-drag-and-drop-auto-scroll@1.4.0:
+    resolution: {integrity: sha512-5GoikoTSW13UX76F9TDeWB8x3jbbGlp/Y+3aRkHe1MOBMkrWkwNpJ42MIVhhX/6NSeaZiPumP0KbGJVs2tOWSQ==}
+    dependencies:
+      '@atlaskit/pragmatic-drag-and-drop': 1.4.0
+      '@babel/runtime': 7.25.7
+    dev: false
+
+  /@atlaskit/pragmatic-drag-and-drop-hitbox@1.0.3:
+    resolution: {integrity: sha512-/Sbu/HqN2VGLYBhnsG7SbRNg98XKkbF6L7XDdBi+izRybfaK1FeMfodPpm/xnBHPJzwYMdkE0qtLyv6afhgMUA==}
+    dependencies:
+      '@atlaskit/pragmatic-drag-and-drop': 1.4.0
+      '@babel/runtime': 7.25.7
+    dev: false
+
+  /@atlaskit/pragmatic-drag-and-drop@1.4.0:
+    resolution: {integrity: sha512-qRY3PTJIcxfl/QB8Gwswz+BRvlmgAC5pB+J2hL6dkIxgqAgVwOhAamMUKsrOcFU/axG2Q7RbNs1xfoLKDuhoPg==}
+    dependencies:
+      '@babel/runtime': 7.25.7
+      bind-event-listener: 3.0.0
+      raf-schd: 4.0.3
+    dev: false
+
   /@babel/code-frame@7.25.7:
     resolution: {integrity: sha512-0xZJFNE5XMpENsgfHYTw8FbX4kv53mFLn2i3XPoq69LyhYSCBJtitaHx9QnsVTrsogI4Z3+HtEfZ2/GFPOtf5g==}
     engines: {node: '>=6.9.0'}
@@ -980,49 +1002,6 @@ packages:
     engines: {node: '>17.0.0'}
     dev: false
 
-  /@dnd-kit/accessibility@3.1.0(react@18.3.1):
-    resolution: {integrity: sha512-ea7IkhKvlJUv9iSHJOnxinBcoOI3ppGnnL+VDJ75O45Nss6HtZd8IdN8touXPDtASfeI2T2LImb8VOZcL47wjQ==}
-    peerDependencies:
-      react: '>=16.8.0'
-    dependencies:
-      react: 18.3.1
-      tslib: 2.7.0
-    dev: false
-
-  /@dnd-kit/core@6.1.0(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-J3cQBClB4TVxwGo3KEjssGEXNJqGVWx17aRTZ1ob0FliR5IjYgTxl5YJbKTzA6IzrtelotH19v6y7uoIRUZPSg==}
-    peerDependencies:
-      react: '>=16.8.0'
-      react-dom: '>=16.8.0'
-    dependencies:
-      '@dnd-kit/accessibility': 3.1.0(react@18.3.1)
-      '@dnd-kit/utilities': 3.2.2(react@18.3.1)
-      react: 18.3.1
-      react-dom: 18.3.1(react@18.3.1)
-      tslib: 2.7.0
-    dev: false
-
-  /@dnd-kit/sortable@8.0.0(@dnd-kit/core@6.1.0)(react@18.3.1):
-    resolution: {integrity: sha512-U3jk5ebVXe1Lr7c2wU7SBZjcWdQP+j7peHJfCspnA81enlu88Mgd7CC8Q+pub9ubP7eKVETzJW+IBAhsqbSu/g==}
-    peerDependencies:
-      '@dnd-kit/core': ^6.1.0
-      react: '>=16.8.0'
-    dependencies:
-      '@dnd-kit/core': 6.1.0(react-dom@18.3.1)(react@18.3.1)
-      '@dnd-kit/utilities': 3.2.2(react@18.3.1)
-      react: 18.3.1
-      tslib: 2.7.0
-    dev: false
-
-  /@dnd-kit/utilities@3.2.2(react@18.3.1):
-    resolution: {integrity: sha512-+MKAJEOfaBe5SmV6t34p80MMKhjvUz0vRrvVJbPT0WElzaOJ/1xs+D+KDv+tD/NE5ujfrChEcshd4fLn0wpiqg==}
-    peerDependencies:
-      react: '>=16.8.0'
-    dependencies:
-      react: 18.3.1
-      tslib: 2.7.0
-    dev: false
-
   /@emotion/babel-plugin@11.12.0:
     resolution: {integrity: sha512-y2WQb+oP8Jqvvclh8Q55gLUyb7UFvgv7eJfsj7td5TToBrIUtPay2kMrZi4xjq9qw2vD0ZR5fSho0yqoFgX7Rw==}
     dependencies:
@@ -4313,6 +4292,10 @@ packages:
       open: 8.4.2
     dev: true
 
+  /bind-event-listener@3.0.0:
+    resolution: {integrity: sha512-PJvH288AWQhKs2v9zyfYdPzlPqf5bXbGMmhmUIY9x4dAUGIWgomO771oBQNwJnMQSnUIXhKu6sgzpBRXTlvb8Q==}
+    dev: false
+
   /bl@4.1.0:
     resolution: {integrity: sha512-1W07cM9gS6DcLperZfFSj+bWLtaPGSOHWhPiGzXmvVJbRLdG82sH/Kn8EtW1VqWVA54AKf2h5k5BbnIbwF3h6w==}
     dependencies:
@@ -7557,6 +7540,10 @@ packages:
     resolution: {integrity: sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A==}
     dev: true
 
+  /raf-schd@4.0.3:
+    resolution: {integrity: sha512-tQkJl2GRWh83ui2DiPTJz9wEiMN20syf+5oKfB03yYP7ioZcJwsIK8FjrtLwH1m7C7e+Tt2yYBlrOpdT+dyeIQ==}
+    dev: false
+
   /raf-throttle@2.0.6:
     resolution: {integrity: sha512-C7W6hy78A+vMmk5a/B6C5szjBHrUzWJkVyakjKCK59Uy2CcA7KhO1JUvvH32IXYFIcyJ3FMKP3ZzCc2/71I6Vg==}
     dev: false

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

@@ -997,6 +997,7 @@
         "controlNetControlMode": "Control Mode",
         "copyImage": "Copy Image",
         "denoisingStrength": "Denoising Strength",
+        "noRasterLayers": "No Raster Layers",
         "downloadImage": "Download Image",
         "general": "General",
         "guidance": "Guidance",
@@ -1412,8 +1413,9 @@
         "paramDenoisingStrength": {
             "heading": "Denoising Strength",
             "paragraphs": [
-                "How much noise is added to the input image.",
-                "0 will result in an identical image, while 1 will result in a completely new image."
+                "Controls how much the generated image varies from the raster layer(s).",
+                "Lower strength stays closer to the combined visible raster layers. Higher strength relies more on the global prompt.",
+                "When there are no raster layers with visible content, this setting is ignored."
             ]
         },
         "paramHeight": {
@@ -1662,6 +1664,7 @@
         "mergeDown": "Merge Down",
         "mergeVisibleOk": "Merged layers",
         "mergeVisibleError": "Error merging layers",
+        "mergingLayers": "Merging layers",
         "clearHistory": "Clear History",
         "bboxOverlay": "Show Bbox Overlay",
         "resetCanvas": "Reset Canvas",
@@ -1774,9 +1777,10 @@
         "newCanvasSession": "New Canvas Session",
         "newCanvasSessionDesc": "This will clear the canvas and all settings except for your model selection. Generations will be staged on the canvas.",
         "replaceCurrent": "Replace Current",
+        "controlLayerEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, or draw on the canvas to get started.",
         "controlMode": {
             "controlMode": "Control Mode",
-            "balanced": "Balanced",
+            "balanced": "Balanced (recommended)",
             "prompt": "Prompt",
             "control": "Control",
             "megaControl": "Mega Control"
@@ -1815,6 +1819,9 @@
             "process": "Process",
             "apply": "Apply",
             "cancel": "Cancel",
+            "advanced": "Advanced",
+            "processingLayerWith": "Processing layer with the {{type}} filter.",
+            "forMoreControl": "For more control, click Advanced below.",
             "spandrel_filter": {
                 "label": "Image-to-Image Model",
                 "description": "Run an image-to-image model on the selected layer.",
@@ -2095,9 +2102,10 @@
     },
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
-        "line1": "<ItalicComponent>Select Object</ItalicComponent> tool for precise object selection and editing",
-        "line2": "Expanded Flux support, now with Global Reference Images",
-        "line3": "Improved tooltips and context menus",
+        "items": [
+            "<StrongComponent>SD 3.5</StrongComponent>: Support for Text-to-Image in Workflows with SD 3.5 Medium and Large.",
+            "<StrongComponent>Canvas</StrongComponent>: Streamlined Control Layer processing and improved default Control settings."
+        ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",
         "watchUiUpdatesOverview": "Watch UI Updates Overview"

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

@@ -8,10 +8,8 @@ import { useSyncLoggingConfig } from 'app/logging/useSyncLoggingConfig';
 import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import type { PartialAppConfig } from 'app/types/invokeai';
-import ImageUploadOverlay from 'common/components/ImageUploadOverlay';
 import { useFocusRegionWatcher } from 'common/hooks/focus';
 import { useClearStorage } from 'common/hooks/useClearStorage';
-import { useFullscreenDropzone } from 'common/hooks/useFullscreenDropzone';
 import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
 import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal';
 import {
@@ -19,6 +17,7 @@ import {
   NewGallerySessionDialog,
 } from 'features/controlLayers/components/NewSessionConfirmationAlertDialog';
 import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
+import { FullscreenDropzone } from 'features/dnd/FullscreenDropzone';
 import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
 import DeleteBoardModal from 'features/gallery/components/Boards/DeleteBoardModal';
 import { ImageContextMenu } from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
@@ -62,8 +61,6 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
   useGetOpenAPISchemaQuery();
   useSyncLoggingConfig();
 
-  const { dropzone, isHandlingUpload, setIsHandlingUpload } = useFullscreenDropzone();
-
   const handleReset = useCallback(() => {
     clearStorage();
     location.reload();
@@ -92,19 +89,8 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
 
   return (
     <ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
-      <Box
-        id="invoke-app-wrapper"
-        w="100dvw"
-        h="100dvh"
-        position="relative"
-        overflow="hidden"
-        {...dropzone.getRootProps()}
-      >
-        <input {...dropzone.getInputProps()} />
+      <Box id="invoke-app-wrapper" w="100dvw" h="100dvh" position="relative" overflow="hidden">
         <AppContent />
-        {dropzone.isDragActive && isHandlingUpload && (
-          <ImageUploadOverlay dropzone={dropzone} setIsHandlingUpload={setIsHandlingUpload} />
-        )}
       </Box>
       <DeleteImageModal />
       <ChangeBoardModal />
@@ -121,6 +107,7 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
       <NewGallerySessionDialog />
       <NewCanvasSessionDialog />
       <ImageContextMenu />
+      <FullscreenDropzone />
     </ErrorBoundary>
   );
 };

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

@@ -1,4 +1,3 @@
-import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppSelector } from 'app/store/storeHooks';
 import { useIsRegionFocused } from 'common/hooks/focus';
 import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
@@ -8,13 +7,11 @@ import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors
 import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
 import { memo } from 'react';
-import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 
 export const GlobalImageHotkeys = memo(() => {
   useAssertSingleton('GlobalImageHotkeys');
-  const lastSelectedImage = useAppSelector(selectLastSelectedImage);
-  const { currentData: imageDTO } = useGetImageDTOQuery(lastSelectedImage?.image_name ?? skipToken);
+  const imageDTO = useAppSelector(selectLastSelectedImage);
 
   if (!imageDTO) {
     return null;

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

@@ -19,7 +19,6 @@ import { $workflowCategories } from 'app/store/nanostores/workflowCategories';
 import { createStore } from 'app/store/store';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import Loading from 'common/components/Loading/Loading';
-import AppDndContext from 'features/dnd/components/AppDndContext';
 import type { WorkflowCategory } from 'features/nodes/types/workflow';
 import type { PropsWithChildren, ReactNode } from 'react';
 import React, { lazy, memo, useEffect, useLayoutEffect, useMemo } from 'react';
@@ -237,9 +236,7 @@ const InvokeAIUI = ({
       <Provider store={store}>
         <React.Suspense fallback={<Loading />}>
           <ThemeLocaleProvider>
-            <AppDndContext>
-              <App config={config} studioInitAction={studioInitAction} />
-            </AppDndContext>
+            <App config={config} studioInitAction={studioInitAction} />
           </ThemeLocaleProvider>
         </React.Suspense>
       </Provider>

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

@@ -17,6 +17,7 @@ const $logger = atom<Logger>(Roarr.child(BASE_CONTEXT));
 export const zLogNamespace = z.enum([
   'canvas',
   'config',
+  'dnd',
   'events',
   'gallery',
   'generation',

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

@@ -1,4 +1,3 @@
 export const STORAGE_PREFIX = '@@invokeai-';
 export const EMPTY_ARRAY = [];
-/** @knipignore */
 export const EMPTY_OBJECT = {};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -16,7 +16,6 @@ import { addGalleryOffsetChangedListener } from 'app/store/middleware/listenerMi
 import { addGetOpenAPISchemaListener } from 'app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema';
 import { addImageAddedToBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageAddedToBoard';
 import { addImageDeletionListeners } from 'app/store/middleware/listenerMiddleware/listeners/imageDeletionListeners';
-import { addImageDroppedListener } from 'app/store/middleware/listenerMiddleware/listeners/imageDropped';
 import { addImageRemovedFromBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageRemovedFromBoard';
 import { addImagesStarredListener } from 'app/store/middleware/listenerMiddleware/listeners/imagesStarred';
 import { addImagesUnstarredListener } from 'app/store/middleware/listenerMiddleware/listeners/imagesUnstarred';
@@ -93,9 +92,6 @@ addGetOpenAPISchemaListener(startAppListening);
 addWorkflowLoadRequestedListener(startAppListening);
 addUpdateAllNodesRequestedListener(startAppListening);
 
-// DND
-addImageDroppedListener(startAppListening);
-
 // Models
 addModelSelectedListener(startAppListening);
 

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

@@ -1,12 +1,12 @@
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { SerializableObject } from 'common/types';
 import { buildAdHocPostProcessingGraph } from 'features/nodes/util/graph/buildAdHocPostProcessingGraph';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { BatchConfig, ImageDTO } from 'services/api/types';
+import type { JsonObject } from 'type-fest';
 
 const log = logger('queue');
 
@@ -39,9 +39,9 @@ export const addAdHocPostProcessingRequestedListener = (startAppListening: AppSt
 
         const enqueueResult = await req.unwrap();
         req.reset();
-        log.debug({ enqueueResult } as SerializableObject, t('queue.graphQueued'));
+        log.debug({ enqueueResult } as JsonObject, t('queue.graphQueued'));
       } catch (error) {
-        log.error({ enqueueBatchArg } as SerializableObject, t('queue.graphFailedToQueue'));
+        log.error({ enqueueBatchArg } as JsonObject, t('queue.graphFailedToQueue'));
 
         if (error instanceof Object && 'status' in error && error.status === 403) {
           return;

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

@@ -1,12 +1,12 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { SerializableObject } from 'common/types';
 import { zPydanticValidationError } from 'features/system/store/zodSchemas';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { truncate, upperFirst } from 'lodash-es';
 import { serializeError } from 'serialize-error';
 import { queueApi } from 'services/api/endpoints/queue';
+import type { JsonObject } from 'type-fest';
 
 const log = logger('queue');
 
@@ -17,7 +17,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
     effect: (action) => {
       const enqueueResult = action.payload;
       const arg = action.meta.arg.originalArgs;
-      log.debug({ enqueueResult } as SerializableObject, 'Batch enqueued');
+      log.debug({ enqueueResult } as JsonObject, 'Batch enqueued');
 
       toast({
         id: 'QUEUE_BATCH_SUCCEEDED',
@@ -45,7 +45,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
           status: 'error',
           description: t('common.unknownError'),
         });
-        log.error({ batchConfig } as SerializableObject, t('queue.batchFailedToQueue'));
+        log.error({ batchConfig } as JsonObject, t('queue.batchFailedToQueue'));
         return;
       }
 
@@ -71,7 +71,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
           description: t('common.unknownError'),
         });
       }
-      log.error({ batchConfig, error: serializeError(response) } as SerializableObject, t('queue.batchFailedToQueue'));
+      log.error({ batchConfig, error: serializeError(response) } as JsonObject, t('queue.batchFailedToQueue'));
     },
   });
 };

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

@@ -1,7 +1,7 @@
 import { logger } from 'app/logging/logger';
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { SerializableObject } from 'common/types';
+import { extractMessageFromAssertionError } from 'common/util/extractMessageFromAssertionError';
 import type { Result } from 'common/util/result';
 import { withResult, withResultAsync } from 'common/util/result';
 import { $canvasManager } from 'features/controlLayers/store/ephemeral';
@@ -10,10 +10,12 @@ import { buildFLUXGraph } from 'features/nodes/util/graph/generation/buildFLUXGr
 import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
 import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
 import type { Graph } from 'features/nodes/util/graph/generation/Graph';
+import { toast } from 'features/toast/toast';
 import { serializeError } from 'serialize-error';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { Invocation } from 'services/api/types';
-import { assert } from 'tsafe';
+import { assert, AssertionError } from 'tsafe';
+import type { JsonObject } from 'type-fest';
 
 const log = logger('generation');
 
@@ -57,7 +59,17 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
       }
 
       if (buildGraphResult.isErr()) {
-        log.error({ error: serializeError(buildGraphResult.error) }, 'Failed to build graph');
+        let description: string | null = null;
+        if (buildGraphResult.error instanceof AssertionError) {
+          description = extractMessageFromAssertionError(buildGraphResult.error);
+        }
+        const error = serializeError(buildGraphResult.error);
+        log.error({ error }, 'Failed to build graph');
+        toast({
+          status: 'error',
+          title: 'Failed to build graph',
+          description,
+        });
         return;
       }
 
@@ -88,7 +100,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
         return;
       }
 
-      log.debug({ batchConfig: prepareBatchResult.value } as SerializableObject, 'Enqueued batch');
+      log.debug({ batchConfig: prepareBatchResult.value } as JsonObject, 'Enqueued batch');
     },
   });
 };

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

@@ -1,12 +1,12 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { SerializableObject } from 'common/types';
 import { parseify } from 'common/util/serialize';
 import { $templates } from 'features/nodes/store/nodesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
 import { serializeError } from 'serialize-error';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
+import type { JsonObject } from 'type-fest';
 
 const log = logger('system');
 
@@ -16,12 +16,12 @@ export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening
     effect: (action, { getState }) => {
       const schemaJSON = action.payload;
 
-      log.debug({ schemaJSON: parseify(schemaJSON) } as SerializableObject, 'Received OpenAPI schema');
+      log.debug({ schemaJSON: parseify(schemaJSON) } as JsonObject, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
 
-      log.debug({ nodeTemplates } as SerializableObject, `Built ${size(nodeTemplates)} node templates`);
+      log.debug({ nodeTemplates } as JsonObject, `Built ${size(nodeTemplates)} node templates`);
 
       $templates.set(nodeTemplates);
     },

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

@@ -1,334 +0,0 @@
-import { createAction } from '@reduxjs/toolkit';
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { deepClone } from 'common/util/deepClone';
-import { selectDefaultControlAdapter, selectDefaultIPAdapter } from 'features/controlLayers/hooks/addLayerHooks';
-import { getPrefixedId } from 'features/controlLayers/konva/util';
-import {
-  controlLayerAdded,
-  entityRasterized,
-  entitySelected,
-  inpaintMaskAdded,
-  rasterLayerAdded,
-  referenceImageAdded,
-  referenceImageIPAdapterImageChanged,
-  rgAdded,
-  rgIPAdapterImageChanged,
-} from 'features/controlLayers/store/canvasSlice';
-import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
-import type {
-  CanvasControlLayerState,
-  CanvasInpaintMaskState,
-  CanvasRasterLayerState,
-  CanvasReferenceImageState,
-  CanvasRegionalGuidanceState,
-} from 'features/controlLayers/store/types';
-import { imageDTOToImageObject, imageDTOToImageWithDims } from 'features/controlLayers/store/util';
-import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
-import { isValidDrop } from 'features/dnd/util/isValidDrop';
-import { imageToCompareChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
-import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
-import { upscaleInitialImageChanged } from 'features/parameters/store/upscaleSlice';
-import { imagesApi } from 'services/api/endpoints/images';
-
-export const dndDropped = createAction<{
-  overData: TypesafeDroppableData;
-  activeData: TypesafeDraggableData;
-}>('dnd/dndDropped');
-
-const log = logger('system');
-
-export const addImageDroppedListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: dndDropped,
-    effect: (action, { dispatch, getState }) => {
-      const { activeData, overData } = action.payload;
-      if (!isValidDrop(overData, activeData)) {
-        return;
-      }
-
-      if (activeData.payloadType === 'IMAGE_DTO') {
-        log.debug({ activeData, overData }, 'Image dropped');
-      } else if (activeData.payloadType === 'GALLERY_SELECTION') {
-        log.debug({ activeData, overData }, `Images (${getState().gallery.selection.length}) dropped`);
-      } else if (activeData.payloadType === 'NODE_FIELD') {
-        log.debug({ activeData, overData }, 'Node field dropped');
-      } else {
-        log.debug({ activeData, overData }, `Unknown payload dropped`);
-      }
-
-      /**
-       * Image dropped on IP Adapter Layer
-       */
-      if (
-        overData.actionType === 'SET_IPA_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { id } = overData.context;
-        dispatch(
-          referenceImageIPAdapterImageChanged({
-            entityIdentifier: { id, type: 'reference_image' },
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
-      /**
-       * Image dropped on RG Layer IP Adapter
-       */
-      if (
-        overData.actionType === 'SET_RG_IP_ADAPTER_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { id, referenceImageId } = overData.context;
-        dispatch(
-          rgIPAdapterImageChanged({
-            entityIdentifier: { id, type: 'regional_guidance' },
-            referenceImageId,
-            imageDTO: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
-      /**
-       * Image dropped on Raster layer
-       */
-      if (
-        overData.actionType === 'ADD_RASTER_LAYER_FROM_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = selectCanvasSlice(getState()).bbox.rect;
-        const overrides: Partial<CanvasRasterLayerState> = {
-          objects: [imageObject],
-          position: { x, y },
-        };
-        dispatch(rasterLayerAdded({ overrides, isSelected: true }));
-        return;
-      }
-
-      /**
-
-      /**
-       * Image dropped on Inpaint Mask
-       */
-      if (
-        overData.actionType === 'ADD_INPAINT_MASK_FROM_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = selectCanvasSlice(getState()).bbox.rect;
-        const overrides: Partial<CanvasInpaintMaskState> = {
-          objects: [imageObject],
-          position: { x, y },
-        };
-        dispatch(inpaintMaskAdded({ overrides, isSelected: true }));
-        return;
-      }
-
-      /**
-
-      /**
-       * Image dropped on Regional Guidance
-       */
-      if (
-        overData.actionType === 'ADD_REGIONAL_GUIDANCE_FROM_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = selectCanvasSlice(getState()).bbox.rect;
-        const overrides: Partial<CanvasRegionalGuidanceState> = {
-          objects: [imageObject],
-          position: { x, y },
-        };
-        dispatch(rgAdded({ overrides, isSelected: true }));
-        return;
-      }
-
-      /**
-       * Image dropped on Raster layer
-       */
-      if (
-        overData.actionType === 'ADD_CONTROL_LAYER_FROM_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const state = getState();
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = selectCanvasSlice(state).bbox.rect;
-        const defaultControlAdapter = selectDefaultControlAdapter(state);
-        const overrides: Partial<CanvasControlLayerState> = {
-          objects: [imageObject],
-          position: { x, y },
-          controlAdapter: defaultControlAdapter,
-        };
-        dispatch(controlLayerAdded({ overrides, isSelected: true }));
-        return;
-      }
-
-      if (
-        overData.actionType === 'ADD_REGIONAL_REFERENCE_IMAGE_FROM_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const state = getState();
-        const ipAdapter = deepClone(selectDefaultIPAdapter(state));
-        ipAdapter.image = imageDTOToImageWithDims(activeData.payload.imageDTO);
-        const overrides: Partial<CanvasRegionalGuidanceState> = {
-          referenceImages: [{ id: getPrefixedId('regional_guidance_reference_image'), ipAdapter }],
-        };
-        dispatch(rgAdded({ overrides, isSelected: true }));
-        return;
-      }
-
-      if (
-        overData.actionType === 'ADD_GLOBAL_REFERENCE_IMAGE_FROM_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const state = getState();
-        const ipAdapter = deepClone(selectDefaultIPAdapter(state));
-        ipAdapter.image = imageDTOToImageWithDims(activeData.payload.imageDTO);
-        const overrides: Partial<CanvasReferenceImageState> = {
-          ipAdapter,
-        };
-        dispatch(referenceImageAdded({ overrides, isSelected: true }));
-        return;
-      }
-
-      /**
-       * Image dropped on Raster layer
-       */
-      if (overData.actionType === 'REPLACE_LAYER_WITH_IMAGE' && activeData.payloadType === 'IMAGE_DTO') {
-        const state = getState();
-        const { entityIdentifier } = overData.context;
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = selectCanvasSlice(state).bbox.rect;
-        dispatch(entityRasterized({ entityIdentifier, imageObject, position: { x, y }, replaceObjects: true }));
-        dispatch(entitySelected({ entityIdentifier }));
-        return;
-      }
-
-      /**
-       * Image dropped on node image field
-       */
-      if (
-        overData.actionType === 'SET_NODES_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { fieldName, nodeId } = overData.context;
-        dispatch(
-          fieldImageValueChanged({
-            nodeId,
-            fieldName,
-            value: activeData.payload.imageDTO,
-          })
-        );
-        return;
-      }
-
-      /**
-       * Image selected for compare
-       */
-      if (
-        overData.actionType === 'SELECT_FOR_COMPARE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { imageDTO } = activeData.payload;
-        dispatch(imageToCompareChanged(imageDTO));
-        return;
-      }
-
-      /**
-       * Image dropped on user board
-       */
-      if (
-        overData.actionType === 'ADD_TO_BOARD' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { imageDTO } = activeData.payload;
-        const { boardId } = overData.context;
-        dispatch(
-          imagesApi.endpoints.addImageToBoard.initiate({
-            imageDTO,
-            board_id: boardId,
-          })
-        );
-        dispatch(selectionChanged([]));
-        return;
-      }
-
-      /**
-       * Image dropped on 'none' board
-       */
-      if (
-        overData.actionType === 'REMOVE_FROM_BOARD' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { imageDTO } = activeData.payload;
-        dispatch(
-          imagesApi.endpoints.removeImageFromBoard.initiate({
-            imageDTO,
-          })
-        );
-        dispatch(selectionChanged([]));
-        return;
-      }
-
-      /**
-       * Image dropped on upscale initial image
-       */
-      if (
-        overData.actionType === 'SET_UPSCALE_INITIAL_IMAGE' &&
-        activeData.payloadType === 'IMAGE_DTO' &&
-        activeData.payload.imageDTO
-      ) {
-        const { imageDTO } = activeData.payload;
-
-        dispatch(upscaleInitialImageChanged(imageDTO));
-        return;
-      }
-
-      /**
-       * Multiple images dropped on user board
-       */
-      if (overData.actionType === 'ADD_TO_BOARD' && activeData.payloadType === 'GALLERY_SELECTION') {
-        const imageDTOs = getState().gallery.selection;
-        const { boardId } = overData.context;
-        dispatch(
-          imagesApi.endpoints.addImagesToBoard.initiate({
-            imageDTOs,
-            board_id: boardId,
-          })
-        );
-        dispatch(selectionChanged([]));
-        return;
-      }
-
-      /**
-       * Multiple images dropped on 'none' board
-       */
-      if (overData.actionType === 'REMOVE_FROM_BOARD' && activeData.payloadType === 'GALLERY_SELECTION') {
-        const imageDTOs = getState().gallery.selection;
-        dispatch(
-          imagesApi.endpoints.removeImagesFromBoard.initiate({
-            imageDTOs,
-          })
-        );
-        dispatch(selectionChanged([]));
-        return;
-      }
-    },
-  });
-};

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

@@ -1,18 +1,8 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import type { RootState } from 'app/store/store';
-import {
-  entityRasterized,
-  entitySelected,
-  referenceImageIPAdapterImageChanged,
-  rgIPAdapterImageChanged,
-} from 'features/controlLayers/store/canvasSlice';
-import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
-import { imageDTOToImageObject } from 'features/controlLayers/store/util';
 import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { boardIdSelected, galleryViewChanged } from 'features/gallery/store/gallerySlice';
-import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
-import { upscaleInitialImageChanged } from 'features/parameters/store/upscaleSlice';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { omit } from 'lodash-es';
@@ -51,93 +41,45 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
 
       log.debug({ imageDTO }, 'Image uploaded');
 
-      const { postUploadAction } = action.meta.arg.originalArgs;
+      const boardId = imageDTO.board_id ?? 'none';
 
-      if (!postUploadAction) {
-        return;
-      }
+      if (action.meta.arg.originalArgs.withToast) {
+        const DEFAULT_UPLOADED_TOAST = {
+          id: 'IMAGE_UPLOADED',
+          title: t('toast.imageUploaded'),
+          status: 'success',
+        } as const;
 
-      const DEFAULT_UPLOADED_TOAST = {
-        id: 'IMAGE_UPLOADED',
-        title: t('toast.imageUploaded'),
-        status: 'success',
-      } as const;
-
-      // default action - just upload and alert user
-      if (postUploadAction.type === 'TOAST') {
-        const boardId = imageDTO.board_id ?? 'none';
+        // default action - just upload and alert user
         if (lastUploadedToastTimeout !== null) {
           window.clearTimeout(lastUploadedToastTimeout);
         }
         const toastApi = toast({
           ...DEFAULT_UPLOADED_TOAST,
-          title: postUploadAction.title || DEFAULT_UPLOADED_TOAST.title,
+          title: DEFAULT_UPLOADED_TOAST.title,
           description: getUploadedToastDescription(boardId, state),
           duration: null, // we will close the toast manually
         });
         lastUploadedToastTimeout = window.setTimeout(() => {
           toastApi.close();
         }, 3000);
-        /**
-         * We only want to change the board and view if this is the first upload of a batch, else we end up hijacking
-         * the user's gallery board and view selection:
-         * - User uploads multiple images
-         * - A couple uploads finish, but others are pending still
-         * - User changes the board selection
-         * - Pending uploads finish and change the board back to the original board
-         * - User is confused as to why the board changed
-         *
-         * Default to true to not require _all_ image upload handlers to set this value
-         */
-        const isFirstUploadOfBatch = action.meta.arg.originalArgs.isFirstUploadOfBatch ?? true;
-        if (isFirstUploadOfBatch) {
-          dispatch(boardIdSelected({ boardId }));
-          dispatch(galleryViewChanged('assets'));
-        }
-        return;
       }
 
-      if (postUploadAction.type === 'SET_UPSCALE_INITIAL_IMAGE') {
-        dispatch(upscaleInitialImageChanged(imageDTO));
-        toast({
-          ...DEFAULT_UPLOADED_TOAST,
-          description: 'set as upscale initial image',
-        });
-        return;
-      }
-
-      if (postUploadAction.type === 'SET_IPA_IMAGE') {
-        const { id } = postUploadAction;
-        dispatch(referenceImageIPAdapterImageChanged({ entityIdentifier: { id, type: 'reference_image' }, imageDTO }));
-        toast({ ...DEFAULT_UPLOADED_TOAST, description: t('toast.setControlImage') });
-        return;
-      }
-
-      if (postUploadAction.type === 'SET_RG_IP_ADAPTER_IMAGE') {
-        const { id, referenceImageId } = postUploadAction;
-        dispatch(
-          rgIPAdapterImageChanged({ entityIdentifier: { id, type: 'regional_guidance' }, referenceImageId, imageDTO })
-        );
-        toast({ ...DEFAULT_UPLOADED_TOAST, description: t('toast.setControlImage') });
-        return;
-      }
-
-      if (postUploadAction.type === 'SET_NODES_IMAGE') {
-        const { nodeId, fieldName } = postUploadAction;
-        dispatch(fieldImageValueChanged({ nodeId, fieldName, value: imageDTO }));
-        toast({ ...DEFAULT_UPLOADED_TOAST, description: `${t('toast.setNodeField')} ${fieldName}` });
-        return;
-      }
-
-      if (postUploadAction.type === 'REPLACE_LAYER_WITH_IMAGE') {
-        const { entityIdentifier } = postUploadAction;
-
-        const state = getState();
-        const imageObject = imageDTOToImageObject(imageDTO);
-        const { x, y } = selectCanvasSlice(state).bbox.rect;
-        dispatch(entityRasterized({ entityIdentifier, imageObject, position: { x, y }, replaceObjects: true }));
-        dispatch(entitySelected({ entityIdentifier }));
-        return;
+      /**
+       * We only want to change the board and view if this is the first upload of a batch, else we end up hijacking
+       * the user's gallery board and view selection:
+       * - User uploads multiple images
+       * - A couple uploads finish, but others are pending still
+       * - User changes the board selection
+       * - Pending uploads finish and change the board back to the original board
+       * - User is confused as to why the board changed
+       *
+       * Default to true to not require _all_ image upload handlers to set this value
+       */
+      const isFirstUploadOfBatch = action.meta.arg.originalArgs.isFirstUploadOfBatch ?? true;
+      if (isFirstUploadOfBatch) {
+        dispatch(boardIdSelected({ boardId }));
+        dispatch(galleryViewChanged('assets'));
       }
     },
   });

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

@@ -1,7 +1,6 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import type { AppDispatch, RootState } from 'app/store/store';
-import type { SerializableObject } from 'common/types';
 import {
   controlLayerModelChanged,
   referenceImageIPAdapterModelChanged,
@@ -41,6 +40,7 @@ import {
   isSpandrelImageToImageModelConfig,
   isT5EncoderModelConfig,
 } from 'services/api/types';
+import type { JsonObject } from 'type-fest';
 
 const log = logger('models');
 
@@ -85,7 +85,7 @@ type ModelHandler = (
   models: AnyModelConfig[],
   state: RootState,
   dispatch: AppDispatch,
-  log: Logger<SerializableObject>
+  log: Logger<JsonObject>
 ) => undefined;
 
 const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
@@ -164,7 +164,7 @@ const handleVAEModels: ModelHandler = (models, state, dispatch, log) => {
   // We have a VAE selected, need to check if it is available
 
   // Grab just the VAE models
-  const vaeModels = models.filter(isNonFluxVAEModelConfig);
+  const vaeModels = models.filter((m) => isNonFluxVAEModelConfig(m));
 
   // If the current VAE model is available, we don't need to do anything
   if (vaeModels.some((m) => m.key === selectedVAEModel.key)) {
@@ -297,7 +297,7 @@ const handleUpscaleModel: ModelHandler = (models, state, dispatch, log) => {
 
 const handleT5EncoderModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedT5EncoderModel = state.params.t5EncoderModel;
-  const t5EncoderModels = models.filter(isT5EncoderModelConfig);
+  const t5EncoderModels = models.filter((m) => isT5EncoderModelConfig(m));
 
   // If the currently selected model is available, we don't need to do anything
   if (selectedT5EncoderModel && t5EncoderModels.some((m) => m.key === selectedT5EncoderModel.key)) {
@@ -325,7 +325,7 @@ const handleT5EncoderModels: ModelHandler = (models, state, dispatch, log) => {
 
 const handleCLIPEmbedModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedCLIPEmbedModel = state.params.clipEmbedModel;
-  const CLIPEmbedModels = models.filter(isCLIPEmbedModelConfig);
+  const CLIPEmbedModels = models.filter((m) => isCLIPEmbedModelConfig(m));
 
   // If the currently selected model is available, we don't need to do anything
   if (selectedCLIPEmbedModel && CLIPEmbedModels.some((m) => m.key === selectedCLIPEmbedModel.key)) {
@@ -353,7 +353,7 @@ const handleCLIPEmbedModels: ModelHandler = (models, state, dispatch, log) => {
 
 const handleFLUXVAEModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedFLUXVAEModel = state.params.fluxVAE;
-  const fluxVAEModels = models.filter(isFluxVAEModelConfig);
+  const fluxVAEModels = models.filter((m) => isFluxVAEModelConfig(m));
 
   // If the currently selected model is available, we don't need to do anything
   if (selectedFLUXVAEModel && fluxVAEModels.some((m) => m.key === selectedFLUXVAEModel.key)) {

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

@@ -4,8 +4,10 @@ import { atom } from 'nanostores';
 /**
  * A fallback non-writable atom that always returns `false`, used when a nanostores atom is only conditionally available
  * in a hook or component.
+ *
+ * @knipignore
  */
-// export const $false: ReadableAtom<boolean> = atom(false);
+export const $false: ReadableAtom<boolean> = atom(false);
 /**
  * A fallback non-writable atom that always returns `true`, used when a nanostores atom is only conditionally available
  * in a hook or component.

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

@@ -3,7 +3,6 @@ import { autoBatchEnhancer, combineReducers, configureStore } from '@reduxjs/too
 import { logger } from 'app/logging/logger';
 import { idbKeyValDriver } from 'app/store/enhancers/reduxRemember/driver';
 import { errorHandler } from 'app/store/enhancers/reduxRemember/errors';
-import type { SerializableObject } from 'common/types';
 import { deepClone } from 'common/util/deepClone';
 import { changeBoardModalSlice } from 'features/changeBoardModal/store/slice';
 import { canvasSettingsPersistConfig, canvasSettingsSlice } from 'features/controlLayers/store/canvasSettingsSlice';
@@ -37,6 +36,7 @@ import undoable from 'redux-undo';
 import { serializeError } from 'serialize-error';
 import { api } from 'services/api';
 import { authToastMiddleware } from 'services/api/authToastMiddleware';
+import type { JsonObject } from 'type-fest';
 
 import { STORAGE_PREFIX } from './constants';
 import { actionSanitizer } from './middleware/devtools/actionSanitizer';
@@ -139,7 +139,7 @@ const unserialize: UnserializeFunction = (data, key) => {
       {
         persistedData: parsed,
         rehydratedData: transformed,
-        diff: diff(parsed, transformed) as SerializableObject, // this is always serializable
+        diff: diff(parsed, transformed) as JsonObject, // this is always serializable
       },
       `Rehydrated slice "${key}"`
     );

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

@@ -1,251 +0,0 @@
-import type { ChakraProps, FlexProps, SystemStyleObject } from '@invoke-ai/ui-library';
-import { Flex, Icon, Image } from '@invoke-ai/ui-library';
-import { IAILoadingImageFallback, IAINoContentFallback } from 'common/components/IAIImageFallback';
-import ImageMetadataOverlay from 'common/components/ImageMetadataOverlay';
-import { useImageUploadButton } from 'common/hooks/useImageUploadButton';
-import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
-import { useImageContextMenu } from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
-import type { MouseEvent, ReactElement, ReactNode, SyntheticEvent } from 'react';
-import { memo, useCallback, useMemo, useRef } from 'react';
-import { PiImageBold, PiUploadSimpleBold } from 'react-icons/pi';
-import type { ImageDTO, PostUploadAction } from 'services/api/types';
-
-import IAIDraggable from './IAIDraggable';
-import IAIDroppable from './IAIDroppable';
-
-const defaultUploadElement = <Icon as={PiUploadSimpleBold} boxSize={16} />;
-
-const defaultNoContentFallback = <IAINoContentFallback icon={PiImageBold} />;
-
-const baseStyles: SystemStyleObject = {
-  touchAction: 'none',
-  userSelect: 'none',
-  webkitUserSelect: 'none',
-};
-
-const sx: SystemStyleObject = {
-  ...baseStyles,
-  '.gallery-image-container::before': {
-    content: '""',
-    display: 'inline-block',
-    position: 'absolute',
-    top: 0,
-    left: 0,
-    right: 0,
-    bottom: 0,
-    pointerEvents: 'none',
-    borderRadius: 'base',
-  },
-  '&[data-selected="selected"]>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-500), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
-  },
-  '&[data-selected="selectedForCompare"]>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-300), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
-  },
-  '&:hover>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 2px var(--invoke-colors-invokeBlue-300), inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-800)',
-  },
-  '&:hover[data-selected="selected"]>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-400), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
-  },
-  '&:hover[data-selected="selectedForCompare"]>.gallery-image-container::before': {
-    boxShadow:
-      'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-200), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
-  },
-};
-
-type IAIDndImageProps = FlexProps & {
-  imageDTO: ImageDTO | undefined;
-  onError?: (event: SyntheticEvent<HTMLImageElement>) => void;
-  onLoad?: (event: SyntheticEvent<HTMLImageElement>) => void;
-  onClick?: (event: MouseEvent<HTMLDivElement>) => void;
-  withMetadataOverlay?: boolean;
-  isDragDisabled?: boolean;
-  isDropDisabled?: boolean;
-  isUploadDisabled?: boolean;
-  minSize?: number;
-  postUploadAction?: PostUploadAction;
-  imageSx?: ChakraProps['sx'];
-  fitContainer?: boolean;
-  droppableData?: TypesafeDroppableData;
-  draggableData?: TypesafeDraggableData;
-  dropLabel?: string;
-  isSelected?: boolean;
-  isSelectedForCompare?: boolean;
-  thumbnail?: boolean;
-  noContentFallback?: ReactElement;
-  useThumbailFallback?: boolean;
-  withHoverOverlay?: boolean;
-  children?: JSX.Element;
-  uploadElement?: ReactNode;
-  dataTestId?: string;
-};
-
-const IAIDndImage = (props: IAIDndImageProps) => {
-  const {
-    imageDTO,
-    onError,
-    onClick,
-    withMetadataOverlay = false,
-    isDropDisabled = false,
-    isDragDisabled = false,
-    isUploadDisabled = false,
-    minSize = 24,
-    postUploadAction,
-    imageSx,
-    fitContainer = false,
-    droppableData,
-    draggableData,
-    dropLabel,
-    isSelected = false,
-    isSelectedForCompare = false,
-    thumbnail = false,
-    noContentFallback = defaultNoContentFallback,
-    uploadElement = defaultUploadElement,
-    useThumbailFallback,
-    withHoverOverlay = false,
-    children,
-    dataTestId,
-    ...rest
-  } = props;
-
-  const openInNewTab = useCallback(
-    (e: MouseEvent) => {
-      if (!imageDTO) {
-        return;
-      }
-      if (e.button !== 1) {
-        return;
-      }
-      window.open(imageDTO.image_url, '_blank');
-    },
-    [imageDTO]
-  );
-
-  const ref = useRef<HTMLDivElement>(null);
-  useImageContextMenu(imageDTO, ref);
-
-  return (
-    <Flex
-      ref={ref}
-      width="full"
-      height="full"
-      alignItems="center"
-      justifyContent="center"
-      position="relative"
-      minW={minSize ? minSize : undefined}
-      minH={minSize ? minSize : undefined}
-      userSelect="none"
-      cursor={isDragDisabled || !imageDTO ? 'default' : 'pointer'}
-      sx={withHoverOverlay ? sx : baseStyles}
-      data-selected={isSelectedForCompare ? 'selectedForCompare' : isSelected ? 'selected' : undefined}
-      {...rest}
-    >
-      {imageDTO && (
-        <Flex
-          className="gallery-image-container"
-          w="full"
-          h="full"
-          position={fitContainer ? 'absolute' : 'relative'}
-          alignItems="center"
-          justifyContent="center"
-        >
-          <Image
-            src={thumbnail ? imageDTO.thumbnail_url : imageDTO.image_url}
-            fallbackStrategy="beforeLoadOrError"
-            fallbackSrc={useThumbailFallback ? imageDTO.thumbnail_url : undefined}
-            fallback={useThumbailFallback ? undefined : <IAILoadingImageFallback image={imageDTO} />}
-            onError={onError}
-            draggable={false}
-            w={imageDTO.width}
-            objectFit="contain"
-            maxW="full"
-            maxH="full"
-            borderRadius="base"
-            sx={imageSx}
-            data-testid={dataTestId}
-          />
-          {withMetadataOverlay && <ImageMetadataOverlay imageDTO={imageDTO} />}
-        </Flex>
-      )}
-      {!imageDTO && !isUploadDisabled && (
-        <UploadButton
-          isUploadDisabled={isUploadDisabled}
-          postUploadAction={postUploadAction}
-          uploadElement={uploadElement}
-          minSize={minSize}
-        />
-      )}
-      {!imageDTO && isUploadDisabled && noContentFallback}
-      {imageDTO && !isDragDisabled && (
-        <IAIDraggable
-          data={draggableData}
-          disabled={isDragDisabled || !imageDTO}
-          onClick={onClick}
-          onAuxClick={openInNewTab}
-        />
-      )}
-      {children}
-      {!isDropDisabled && <IAIDroppable data={droppableData} disabled={isDropDisabled} dropLabel={dropLabel} />}
-    </Flex>
-  );
-};
-
-export default memo(IAIDndImage);
-
-const UploadButton = memo(
-  ({
-    isUploadDisabled,
-    postUploadAction,
-    uploadElement,
-    minSize,
-  }: {
-    isUploadDisabled: boolean;
-    postUploadAction?: PostUploadAction;
-    uploadElement: ReactNode;
-    minSize: number;
-  }) => {
-    const { getUploadButtonProps, getUploadInputProps } = useImageUploadButton({
-      postUploadAction,
-      isDisabled: isUploadDisabled,
-    });
-
-    const uploadButtonStyles = useMemo<SystemStyleObject>(() => {
-      const styles: SystemStyleObject = {
-        minH: minSize,
-        w: 'full',
-        h: 'full',
-        alignItems: 'center',
-        justifyContent: 'center',
-        borderRadius: 'base',
-        transitionProperty: 'common',
-        transitionDuration: '0.1s',
-        color: 'base.500',
-      };
-      if (!isUploadDisabled) {
-        Object.assign(styles, {
-          cursor: 'pointer',
-          bg: 'base.700',
-          _hover: {
-            bg: 'base.650',
-            color: 'base.300',
-          },
-        });
-      }
-      return styles;
-    }, [isUploadDisabled, minSize]);
-
-    return (
-      <Flex sx={uploadButtonStyles} {...getUploadButtonProps()}>
-        <input {...getUploadInputProps()} />
-        {uploadElement}
-      </Flex>
-    );
-  }
-);
-
-UploadButton.displayName = 'UploadButton';

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

@@ -1,38 +0,0 @@
-import type { BoxProps } from '@invoke-ai/ui-library';
-import { Box } from '@invoke-ai/ui-library';
-import { useDraggableTypesafe } from 'features/dnd/hooks/typesafeHooks';
-import type { TypesafeDraggableData } from 'features/dnd/types';
-import { memo, useRef } from 'react';
-import { v4 as uuidv4 } from 'uuid';
-
-type IAIDraggableProps = BoxProps & {
-  disabled?: boolean;
-  data?: TypesafeDraggableData;
-};
-
-const IAIDraggable = (props: IAIDraggableProps) => {
-  const { data, disabled, ...rest } = props;
-  const dndId = useRef(uuidv4());
-
-  const { attributes, listeners, setNodeRef } = useDraggableTypesafe({
-    id: dndId.current,
-    disabled,
-    data,
-  });
-
-  return (
-    <Box
-      ref={setNodeRef}
-      position="absolute"
-      w="full"
-      h="full"
-      top={0}
-      insetInlineStart={0}
-      {...attributes}
-      {...listeners}
-      {...rest}
-    />
-  );
-};
-
-export default memo(IAIDraggable);

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

@@ -1,64 +0,0 @@
-import { Flex, Text } from '@invoke-ai/ui-library';
-import { memo } from 'react';
-
-type Props = {
-  isOver: boolean;
-  label?: string;
-  withBackdrop?: boolean;
-};
-
-const IAIDropOverlay = (props: Props) => {
-  const { isOver, label, withBackdrop = true } = props;
-  return (
-    <Flex position="absolute" top={0} right={0} bottom={0} left={0}>
-      <Flex
-        position="absolute"
-        top={0}
-        right={0}
-        bottom={0}
-        left={0}
-        w="full"
-        h="full"
-        bg={withBackdrop ? 'base.900' : 'transparent'}
-        opacity={0.7}
-        borderRadius="base"
-        alignItems="center"
-        justifyContent="center"
-        transitionProperty="common"
-        transitionDuration="0.1s"
-      />
-
-      <Flex
-        position="absolute"
-        top={0.5}
-        right={0.5}
-        bottom={0.5}
-        left={0.5}
-        opacity={1}
-        borderWidth={1.5}
-        borderColor={isOver ? 'invokeYellow.300' : 'base.500'}
-        borderRadius="base"
-        borderStyle="dashed"
-        transitionProperty="common"
-        transitionDuration="0.1s"
-        alignItems="center"
-        justifyContent="center"
-      >
-        {label && (
-          <Text
-            fontSize="lg"
-            fontWeight="semibold"
-            color={isOver ? 'invokeYellow.300' : 'base.500'}
-            transitionProperty="common"
-            transitionDuration="0.1s"
-            textAlign="center"
-          >
-            {label}
-          </Text>
-        )}
-      </Flex>
-    </Flex>
-  );
-};
-
-export default memo(IAIDropOverlay);

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

@@ -1,46 +0,0 @@
-import { Box } from '@invoke-ai/ui-library';
-import { useDroppableTypesafe } from 'features/dnd/hooks/typesafeHooks';
-import type { TypesafeDroppableData } from 'features/dnd/types';
-import { isValidDrop } from 'features/dnd/util/isValidDrop';
-import { AnimatePresence } from 'framer-motion';
-import { memo, useRef } from 'react';
-import { v4 as uuidv4 } from 'uuid';
-
-import IAIDropOverlay from './IAIDropOverlay';
-
-type IAIDroppableProps = {
-  dropLabel?: string;
-  disabled?: boolean;
-  data?: TypesafeDroppableData;
-};
-
-const IAIDroppable = (props: IAIDroppableProps) => {
-  const { dropLabel, data, disabled } = props;
-  const dndId = useRef(uuidv4());
-
-  const { isOver, setNodeRef, active } = useDroppableTypesafe({
-    id: dndId.current,
-    disabled,
-    data,
-  });
-
-  return (
-    <Box
-      ref={setNodeRef}
-      position="absolute"
-      top={0}
-      right={0}
-      bottom={0}
-      left={0}
-      w="full"
-      h="full"
-      pointerEvents={active ? 'auto' : 'none'}
-    >
-      <AnimatePresence>
-        {isValidDrop(data, active?.data.current) && <IAIDropOverlay isOver={isOver} label={dropLabel} />}
-      </AnimatePresence>
-    </Box>
-  );
-};
-
-export default memo(IAIDroppable);

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

@@ -1,24 +0,0 @@
-import type { SystemStyleObject } from '@invoke-ai/ui-library';
-import { Box, Skeleton } from '@invoke-ai/ui-library';
-import { memo } from 'react';
-
-const skeletonStyles: SystemStyleObject = {
-  position: 'relative',
-  height: 'full',
-  width: 'full',
-  '::before': {
-    content: "''",
-    display: 'block',
-    pt: '100%',
-  },
-};
-
-const IAIFillSkeleton = () => {
-  return (
-    <Skeleton sx={skeletonStyles}>
-      <Box position="absolute" top={0} insetInlineStart={0} height="full" width="full" />
-    </Skeleton>
-  );
-};
-
-export default memo(IAIFillSkeleton);

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

@@ -6,7 +6,7 @@ import type { ImageDTO } from 'services/api/types';
 
 type Props = { image: ImageDTO | undefined };
 
-export const IAILoadingImageFallback = memo((props: Props) => {
+const IAILoadingImageFallback = memo((props: Props) => {
   if (props.image) {
     return (
       <Skeleton

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

@@ -1,28 +0,0 @@
-import { Badge, Flex } from '@invoke-ai/ui-library';
-import { memo } from 'react';
-import type { ImageDTO } from 'services/api/types';
-
-type ImageMetadataOverlayProps = {
-  imageDTO: ImageDTO;
-};
-
-const ImageMetadataOverlay = ({ imageDTO }: ImageMetadataOverlayProps) => {
-  return (
-    <Flex
-      pointerEvents="none"
-      flexDirection="column"
-      position="absolute"
-      top={0}
-      insetInlineStart={0}
-      p={2}
-      alignItems="flex-start"
-      gap={2}
-    >
-      <Badge variant="solid" colorScheme="base">
-        {imageDTO.width} × {imageDTO.height}
-      </Badge>
-    </Flex>
-  );
-};
-
-export default memo(ImageMetadataOverlay);

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

@@ -1,89 +0,0 @@
-import { Box, Flex, Heading } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { selectSelectedBoardId } from 'features/gallery/store/gallerySelectors';
-import { selectMaxImageUploadCount } from 'features/system/store/configSlice';
-import { memo } from 'react';
-import type { DropzoneState } from 'react-dropzone';
-import { useHotkeys } from 'react-hotkeys-hook';
-import { useTranslation } from 'react-i18next';
-import { useBoardName } from 'services/api/hooks/useBoardName';
-
-type ImageUploadOverlayProps = {
-  dropzone: DropzoneState;
-  setIsHandlingUpload: (isHandlingUpload: boolean) => void;
-};
-
-const ImageUploadOverlay = (props: ImageUploadOverlayProps) => {
-  const { dropzone, setIsHandlingUpload } = props;
-
-  useHotkeys(
-    'esc',
-    () => {
-      setIsHandlingUpload(false);
-    },
-    [setIsHandlingUpload]
-  );
-
-  return (
-    <Box position="absolute" top={0} right={0} bottom={0} left={0} zIndex={999} backdropFilter="blur(20px)">
-      <Flex position="absolute" top={0} right={0} bottom={0} left={0} bg="base.900" opacity={0.7} />
-      <Flex
-        position="absolute"
-        flexDir="column"
-        gap={4}
-        top={2}
-        right={2}
-        bottom={2}
-        left={2}
-        opacity={1}
-        borderWidth={2}
-        borderColor={dropzone.isDragAccept ? 'invokeYellow.300' : 'error.500'}
-        borderRadius="base"
-        borderStyle="dashed"
-        transitionProperty="common"
-        transitionDuration="0.1s"
-        alignItems="center"
-        justifyContent="center"
-        color={dropzone.isDragReject ? 'error.300' : undefined}
-      >
-        {dropzone.isDragAccept && <DragAcceptMessage />}
-        {!dropzone.isDragAccept && <DragRejectMessage />}
-      </Flex>
-    </Box>
-  );
-};
-export default memo(ImageUploadOverlay);
-
-const DragAcceptMessage = () => {
-  const { t } = useTranslation();
-  const selectedBoardId = useAppSelector(selectSelectedBoardId);
-  const boardName = useBoardName(selectedBoardId);
-
-  return (
-    <>
-      <Heading size="lg">{t('gallery.dropToUpload')}</Heading>
-      <Heading size="md">{t('toast.imagesWillBeAddedTo', { boardName })}</Heading>
-    </>
-  );
-};
-
-const DragRejectMessage = () => {
-  const { t } = useTranslation();
-  const maxImageUploadCount = useAppSelector(selectMaxImageUploadCount);
-
-  if (maxImageUploadCount === undefined) {
-    return (
-      <>
-        <Heading size="lg">{t('toast.invalidUpload')}</Heading>
-        <Heading size="md">{t('toast.uploadFailedInvalidUploadDesc')}</Heading>
-      </>
-    );
-  }
-
-  return (
-    <>
-      <Heading size="lg">{t('toast.invalidUpload')}</Heading>
-      <Heading size="md">{t('toast.uploadFailedInvalidUploadDesc_withCount', { count: maxImageUploadCount })}</Heading>
-    </>
-  );
-};

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

@@ -1,5 +1,6 @@
 import type { PopoverProps } from '@invoke-ai/ui-library';
 import commercialLicenseBg from 'public/assets/images/commercial-license-bg.png';
+import denoisingStrength from 'public/assets/images/denoising-strength.png';
 
 export type Feature =
   | 'clipSkip'
@@ -125,7 +126,7 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
     href: 'https://support.invoke.ai/support/solutions/articles/151000158838-compositing-settings',
   },
   infillMethod: {
-    href: 'https://support.invoke.ai/support/solutions/articles/151000158841-infill-and-scaling',
+    href: 'https://support.invoke.ai/support/solutions/articles/151000158838-compositing-settings',
   },
   scaleBeforeProcessing: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000158841',
@@ -138,6 +139,7 @@ export const POPOVER_DATA: { [key in Feature]?: PopoverData } = {
   },
   paramDenoisingStrength: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000094998-image-to-image',
+    image: denoisingStrength,
   },
   paramHrf: {
     href: 'https://support.invoke.ai/support/solutions/articles/151000096700-how-can-i-get-larger-images-what-does-upscaling-do-',

invokeai/frontend/web/src/common/components/OverlayScrollbars/ScrollableContent.tsx

@@ -1,9 +1,13 @@
+import { combine } from '@atlaskit/pragmatic-drag-and-drop/combine';
+import { autoScrollForElements } from '@atlaskit/pragmatic-drag-and-drop-auto-scroll/element';
+import { autoScrollForExternal } from '@atlaskit/pragmatic-drag-and-drop-auto-scroll/external';
 import type { ChakraProps } from '@invoke-ai/ui-library';
 import { Box, Flex } from '@invoke-ai/ui-library';
 import { getOverlayScrollbarsParams } from 'common/components/OverlayScrollbars/constants';
+import type { OverlayScrollbarsComponentRef } from 'overlayscrollbars-react';
 import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';
 import type { CSSProperties, PropsWithChildren } from 'react';
-import { memo, useMemo } from 'react';
+import { memo, useEffect, useMemo, useState } from 'react';
 
 type Props = PropsWithChildren & {
   maxHeight?: ChakraProps['maxHeight'];
@@ -11,17 +15,38 @@ type Props = PropsWithChildren & {
   overflowY?: 'hidden' | 'scroll';
 };
 
-const styles: CSSProperties = { height: '100%', width: '100%' };
+const styles: CSSProperties = { position: 'absolute', top: 0, left: 0, right: 0, bottom: 0 };
 
 const ScrollableContent = ({ children, maxHeight, overflowX = 'hidden', overflowY = 'scroll' }: Props) => {
   const overlayscrollbarsOptions = useMemo(
     () => getOverlayScrollbarsParams(overflowX, overflowY).options,
     [overflowX, overflowY]
   );
+  const [os, osRef] = useState<OverlayScrollbarsComponentRef | null>(null);
+  useEffect(() => {
+    const osInstance = os?.osInstance();
+
+    if (!osInstance) {
+      return;
+    }
+
+    const element = osInstance.elements().viewport;
+
+    // `pragmatic-drag-and-drop-auto-scroll` requires the element to have `overflow-y: scroll` or `overflow-y: auto`
+    // else it logs an ugly warning. In our case, using a custom scrollbar library, it will be 'hidden' by default.
+    // To prevent the erroneous warning, we temporarily set the overflow-y to 'scroll' and then revert it back.
+    const overflowY = element.style.overflowY; // starts 'hidden'
+    element.style.setProperty('overflow-y', 'scroll', 'important');
+    const cleanup = combine(autoScrollForElements({ element }), autoScrollForExternal({ element }));
+    element.style.setProperty('overflow-y', overflowY);
+
+    return cleanup;
+  }, [os]);
+
   return (
     <Flex w="full" h="full" maxHeight={maxHeight} position="relative">
       <Box position="absolute" top={0} left={0} right={0} bottom={0}>
-        <OverlayScrollbarsComponent defer style={styles} options={overlayscrollbarsOptions}>
+        <OverlayScrollbarsComponent ref={osRef} style={styles} options={overlayscrollbarsOptions}>
           {children}
         </OverlayScrollbarsComponent>
       </Box>

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

@@ -0,0 +1,57 @@
+type Props = {
+  /**
+   * The amplitude of the wave. 0 is a straight line, higher values create more pronounced waves.
+   */
+  amplitude: number;
+  /**
+   * The number of segments in the line. More segments create a smoother wave.
+   */
+  segments?: number;
+  /**
+   * The color of the wave.
+   */
+  stroke: string;
+  /**
+   * The width of the wave.
+   */
+  strokeWidth: number;
+  /**
+   * The width of the SVG.
+   */
+  width: number;
+  /**
+   * The height of the SVG.
+   */
+  height: number;
+};
+
+const WavyLine = ({ amplitude, stroke, strokeWidth, width, height, segments = 5 }: Props) => {
+  // Calculate the path dynamically based on waviness
+  const generatePath = () => {
+    if (amplitude === 0) {
+      // If waviness is 0, return a straight line
+      return `M0,${height / 2} L${width},${height / 2}`;
+    }
+
+    const clampedAmplitude = Math.min(height / 2, amplitude); // Cap amplitude to half the height
+    const segmentWidth = width / segments;
+    let path = `M0,${height / 2}`; // Start in the middle of the left edge
+
+    // Loop through each segment and alternate the y position to create waves
+    for (let i = 1; i <= segments; i++) {
+      const x = i * segmentWidth;
+      const y = height / 2 + (i % 2 === 0 ? clampedAmplitude : -clampedAmplitude);
+      path += ` Q${x - segmentWidth / 2},${y} ${x},${height / 2}`;
+    }
+
+    return path;
+  };
+
+  return (
+    <svg width={width} height={height} viewBox={`0 0 ${width} ${height}`} xmlns="http://www.w3.org/2000/svg">
+      <path d={generatePath()} fill="none" stroke={stroke} strokeWidth={strokeWidth} />
+    </svg>
+  );
+};
+
+export default WavyLine;

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

@@ -1,124 +0,0 @@
-import { logger } from 'app/logging/logger';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
-import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
-import { selectMaxImageUploadCount } from 'features/system/store/configSlice';
-import { toast } from 'features/toast/toast';
-import { selectActiveTab } from 'features/ui/store/uiSelectors';
-import { useCallback, useEffect, useState } from 'react';
-import type { Accept, FileRejection } from 'react-dropzone';
-import { useDropzone } from 'react-dropzone';
-import { useTranslation } from 'react-i18next';
-import { useUploadImageMutation } from 'services/api/endpoints/images';
-import type { PostUploadAction } from 'services/api/types';
-
-const log = logger('gallery');
-
-const accept: Accept = {
-  'image/png': ['.png'],
-  'image/jpeg': ['.jpg', '.jpeg', '.png'],
-};
-
-export const useFullscreenDropzone = () => {
-  useAssertSingleton('useFullscreenDropzone');
-  const { t } = useTranslation();
-  const autoAddBoardId = useAppSelector(selectAutoAddBoardId);
-  const [isHandlingUpload, setIsHandlingUpload] = useState<boolean>(false);
-  const [uploadImage] = useUploadImageMutation();
-  const activeTabName = useAppSelector(selectActiveTab);
-  const maxImageUploadCount = useAppSelector(selectMaxImageUploadCount);
-
-  const getPostUploadAction = useCallback((): PostUploadAction => {
-    if (activeTabName === 'upscaling') {
-      return { type: 'SET_UPSCALE_INITIAL_IMAGE' };
-    } else {
-      return { type: 'TOAST' };
-    }
-  }, [activeTabName]);
-
-  const onDrop = useCallback(
-    (acceptedFiles: Array<File>, fileRejections: Array<FileRejection>) => {
-      if (fileRejections.length > 0) {
-        const errors = fileRejections.map((rejection) => ({
-          errors: rejection.errors.map(({ message }) => message),
-          file: rejection.file.path,
-        }));
-        log.error({ errors }, 'Invalid upload');
-        const description =
-          maxImageUploadCount === undefined
-            ? t('toast.uploadFailedInvalidUploadDesc')
-            : t('toast.uploadFailedInvalidUploadDesc_withCount', { count: maxImageUploadCount });
-
-        toast({
-          id: 'UPLOAD_FAILED',
-          title: t('toast.uploadFailed'),
-          description,
-          status: 'error',
-        });
-
-        setIsHandlingUpload(false);
-        return;
-      }
-
-      for (const [i, file] of acceptedFiles.entries()) {
-        uploadImage({
-          file,
-          image_category: 'user',
-          is_intermediate: false,
-          postUploadAction: getPostUploadAction(),
-          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-          // The `imageUploaded` listener does some extra logic, like switching to the asset view on upload on the
-          // first upload of a "batch".
-          isFirstUploadOfBatch: i === 0,
-        });
-      }
-
-      setIsHandlingUpload(false);
-    },
-    [t, maxImageUploadCount, uploadImage, getPostUploadAction, autoAddBoardId]
-  );
-
-  const onDragOver = useCallback(() => {
-    setIsHandlingUpload(true);
-  }, []);
-
-  const onDragLeave = useCallback(() => {
-    setIsHandlingUpload(false);
-  }, []);
-
-  const dropzone = useDropzone({
-    accept,
-    noClick: true,
-    onDrop,
-    onDragOver,
-    onDragLeave,
-    noKeyboard: true,
-    multiple: maxImageUploadCount === undefined || maxImageUploadCount > 1,
-    maxFiles: maxImageUploadCount,
-  });
-
-  useEffect(() => {
-    // This is a hack to allow pasting images into the uploader
-    const handlePaste = (e: ClipboardEvent) => {
-      if (!dropzone.inputRef.current) {
-        return;
-      }
-
-      if (e.clipboardData?.files) {
-        // Set the files on the dropzone.inputRef
-        dropzone.inputRef.current.files = e.clipboardData.files;
-        // Dispatch the change event, dropzone catches this and we get to use its own validation
-        dropzone.inputRef.current?.dispatchEvent(new Event('change', { bubbles: true }));
-      }
-    };
-
-    // Add the paste event listener
-    document.addEventListener('paste', handlePaste);
-
-    return () => {
-      document.removeEventListener('paste', handlePaste);
-    };
-  }, [dropzone.inputRef]);
-
-  return { dropzone, isHandlingUpload, setIsHandlingUpload };
-};

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

@@ -1,3 +1,5 @@
+import type { IconButtonProps, SystemStyleObject } from '@invoke-ai/ui-library';
+import { IconButton } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import { useAppSelector } from 'app/store/storeHooks';
 import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
@@ -7,14 +9,23 @@ import { useCallback } from 'react';
 import type { FileRejection } from 'react-dropzone';
 import { useDropzone } from 'react-dropzone';
 import { useTranslation } from 'react-i18next';
-import { useUploadImageMutation } from 'services/api/endpoints/images';
-import type { PostUploadAction } from 'services/api/types';
+import { PiUploadBold } from 'react-icons/pi';
+import { uploadImages, useUploadImageMutation } from 'services/api/endpoints/images';
+import type { ImageDTO } from 'services/api/types';
+import { assert } from 'tsafe';
+import type { SetOptional } from 'type-fest';
 
-type UseImageUploadButtonArgs = {
-  postUploadAction?: PostUploadAction;
-  isDisabled?: boolean;
-  allowMultiple?: boolean;
-};
+type UseImageUploadButtonArgs =
+  | {
+      isDisabled?: boolean;
+      allowMultiple: false;
+      onUpload?: (imageDTO: ImageDTO) => void;
+    }
+  | {
+      isDisabled?: boolean;
+      allowMultiple: true;
+      onUpload?: (imageDTOs: ImageDTO[]) => void;
+    };
 
 const log = logger('gallery');
 
@@ -37,30 +48,46 @@ const log = logger('gallery');
  * <Button {...getUploadButtonProps()} /> // will open the file dialog on click
  * <input {...getUploadInputProps()} /> // hidden, handles native upload functionality
  */
-export const useImageUploadButton = ({
-  postUploadAction,
-  isDisabled,
-  allowMultiple = false,
-}: UseImageUploadButtonArgs) => {
+export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: UseImageUploadButtonArgs) => {
   const autoAddBoardId = useAppSelector(selectAutoAddBoardId);
-  const [uploadImage] = useUploadImageMutation();
+  const [uploadImage, request] = useUploadImageMutation();
   const maxImageUploadCount = useAppSelector(selectMaxImageUploadCount);
   const { t } = useTranslation();
 
   const onDropAccepted = useCallback(
-    (files: File[]) => {
-      for (const [i, file] of files.entries()) {
-        uploadImage({
+    async (files: File[]) => {
+      if (!allowMultiple) {
+        if (files.length > 1) {
+          log.warn('Multiple files dropped but only one allowed');
+          return;
+        }
+        const file = files[0];
+        assert(file !== undefined); // should never happen
+        const imageDTO = await uploadImage({
           file,
           image_category: 'user',
           is_intermediate: false,
-          postUploadAction: postUploadAction ?? { type: 'TOAST' },
           board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-          isFirstUploadOfBatch: i === 0,
-        });
+        }).unwrap();
+        if (onUpload) {
+          onUpload(imageDTO);
+        }
+      } else {
+        //
+        const imageDTOs = await uploadImages(
+          files.map((file) => ({
+            file,
+            image_category: 'user',
+            is_intermediate: false,
+            board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          }))
+        );
+        if (onUpload) {
+          onUpload(imageDTOs);
+        }
       }
     },
-    [autoAddBoardId, postUploadAction, uploadImage]
+    [allowMultiple, autoAddBoardId, onUpload, uploadImage]
   );
 
   const onDropRejected = useCallback(
@@ -103,5 +130,42 @@ export const useImageUploadButton = ({
     maxFiles: maxImageUploadCount,
   });
 
-  return { getUploadButtonProps, getUploadInputProps, openUploader };
+  return { getUploadButtonProps, getUploadInputProps, openUploader, request };
+};
+
+const sx = {
+  borderColor: 'error.500',
+  borderStyle: 'solid',
+  borderWidth: 0,
+  borderRadius: 'base',
+  '&[data-error=true]': {
+    borderWidth: 1,
+  },
+} satisfies SystemStyleObject;
+
+export const UploadImageButton = ({
+  isDisabled = false,
+  onUpload,
+  isError = false,
+  ...rest
+}: {
+  onUpload?: (imageDTO: ImageDTO) => void;
+  isError?: boolean;
+} & SetOptional<IconButtonProps, 'aria-label'>) => {
+  const uploadApi = useImageUploadButton({ isDisabled, allowMultiple: false, onUpload });
+  return (
+    <>
+      <IconButton
+        aria-label="Upload image"
+        variant="ghost"
+        sx={sx}
+        data-error={isError}
+        icon={<PiUploadBold />}
+        isLoading={uploadApi.request.isLoading}
+        {...rest}
+        {...uploadApi.getUploadButtonProps()}
+      />
+      <input {...uploadApi.getUploadInputProps()} />
+    </>
+  );
 };

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

@@ -1,14 +0,0 @@
-import { getPrefixedId, nanoid } from 'features/controlLayers/konva/util';
-import { useMemo } from 'react';
-
-export const useNanoid = (prefix?: string) => {
-  const id = useMemo(() => {
-    if (prefix) {
-      return getPrefixedId(prefix);
-    } else {
-      return nanoid();
-    }
-  }, [prefix]);
-
-  return id;
-};

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

@@ -1,12 +0,0 @@
-type SerializableValue =
-  | string
-  | number
-  | boolean
-  | null
-  | undefined
-  | SerializableValue[]
-  | readonly SerializableValue[]
-  | SerializableObject;
-export type SerializableObject = {
-  [k: string | number]: SerializableValue;
-};

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

@@ -0,0 +1,6 @@
+import type { AssertionError } from 'tsafe';
+
+export function extractMessageFromAssertionError(error: AssertionError): string | null {
+  const match = error.message.match(/Wrong assertion encountered: "(.*)"/);
+  return match ? (match[1] ?? null) : null;
+}

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

@@ -0,0 +1,15 @@
+import type { CSSProperties } from 'react';
+
+/**
+ * Chakra's Tooltip's method of finding the nearest scroll parent has a problem - it assumes the first parent with
+ * `overflow: hidden` is the scroll parent. In this case, the Collapse component has that style, but isn't scrollable
+ * itself. The result is that the tooltip does not close on scroll, because the scrolling happens higher up in the DOM.
+ *
+ * As a hacky workaround, we can set the overflow to `visible`, which allows the scroll parent search to continue up to
+ * the actual scroll parent (in this case, the OverlayScrollbarsComponent in BoardsListWrapper).
+ *
+ * See: https://github.com/chakra-ui/chakra-ui/issues/7871#issuecomment-2453780958
+ */
+export const fixTooltipCloseOnScrollStyles: CSSProperties = {
+  overflow: 'visible',
+};

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

@@ -1,38 +1,26 @@
 import { Grid, GridItem } from '@invoke-ai/ui-library';
-import IAIDroppable from 'common/components/IAIDroppable';
-import type {
-  AddControlLayerFromImageDropData,
-  AddGlobalReferenceImageFromImageDropData,
-  AddRasterLayerFromImageDropData,
-  AddRegionalReferenceImageFromImageDropData,
-} from 'features/dnd/types';
+import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
+import { newCanvasEntityFromImageDndTarget } from 'features/dnd/dnd';
+import { DndDropTarget } from 'features/dnd/DndDropTarget';
 import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 
-const addRasterLayerFromImageDropData: AddRasterLayerFromImageDropData = {
-  id: 'add-raster-layer-from-image-drop-data',
-  actionType: 'ADD_RASTER_LAYER_FROM_IMAGE',
-};
-
-const addControlLayerFromImageDropData: AddControlLayerFromImageDropData = {
-  id: 'add-control-layer-from-image-drop-data',
-  actionType: 'ADD_CONTROL_LAYER_FROM_IMAGE',
-};
-
-const addRegionalReferenceImageFromImageDropData: AddRegionalReferenceImageFromImageDropData = {
-  id: 'add-control-layer-from-image-drop-data',
-  actionType: 'ADD_REGIONAL_REFERENCE_IMAGE_FROM_IMAGE',
-};
-
-const addGlobalReferenceImageFromImageDropData: AddGlobalReferenceImageFromImageDropData = {
-  id: 'add-control-layer-from-image-drop-data',
-  actionType: 'ADD_GLOBAL_REFERENCE_IMAGE_FROM_IMAGE',
-};
+const addRasterLayerFromImageDndTargetData = newCanvasEntityFromImageDndTarget.getData({ type: 'raster_layer' });
+const addControlLayerFromImageDndTargetData = newCanvasEntityFromImageDndTarget.getData({
+  type: 'control_layer',
+});
+const addRegionalGuidanceReferenceImageFromImageDndTargetData = newCanvasEntityFromImageDndTarget.getData({
+  type: 'regional_guidance_with_reference_image',
+});
+const addGlobalReferenceImageFromImageDndTargetData = newCanvasEntityFromImageDndTarget.getData({
+  type: 'reference_image',
+});
 
 export const CanvasDropArea = memo(() => {
   const { t } = useTranslation();
   const imageViewer = useImageViewer();
+  const isBusy = useCanvasIsBusy();
 
   if (imageViewer.isOpen) {
     return null;
@@ -51,28 +39,36 @@ export const CanvasDropArea = memo(() => {
         pointerEvents="none"
       >
         <GridItem position="relative">
-          <IAIDroppable
-            dropLabel={t('controlLayers.canvasContextMenu.newRasterLayer')}
-            data={addRasterLayerFromImageDropData}
+          <DndDropTarget
+            dndTarget={newCanvasEntityFromImageDndTarget}
+            dndTargetData={addRasterLayerFromImageDndTargetData}
+            label={t('controlLayers.canvasContextMenu.newRasterLayer')}
+            isDisabled={isBusy}
           />
         </GridItem>
         <GridItem position="relative">
-          <IAIDroppable
-            dropLabel={t('controlLayers.canvasContextMenu.newControlLayer')}
-            data={addControlLayerFromImageDropData}
+          <DndDropTarget
+            dndTarget={newCanvasEntityFromImageDndTarget}
+            dndTargetData={addControlLayerFromImageDndTargetData}
+            label={t('controlLayers.canvasContextMenu.newControlLayer')}
+            isDisabled={isBusy}
           />
         </GridItem>
 
         <GridItem position="relative">
-          <IAIDroppable
-            dropLabel={t('controlLayers.canvasContextMenu.newRegionalReferenceImage')}
-            data={addRegionalReferenceImageFromImageDropData}
+          <DndDropTarget
+            dndTarget={newCanvasEntityFromImageDndTarget}
+            dndTargetData={addRegionalGuidanceReferenceImageFromImageDndTargetData}
+            label={t('controlLayers.canvasContextMenu.newRegionalReferenceImage')}
+            isDisabled={isBusy}
           />
         </GridItem>
         <GridItem position="relative">
-          <IAIDroppable
-            dropLabel={t('controlLayers.canvasContextMenu.newGlobalReferenceImage')}
-            data={addGlobalReferenceImageFromImageDropData}
+          <DndDropTarget
+            dndTarget={newCanvasEntityFromImageDndTarget}
+            dndTargetData={addGlobalReferenceImageFromImageDndTargetData}
+            label={t('controlLayers.canvasContextMenu.newGlobalReferenceImage')}
+            isDisabled={isBusy}
           />
         </GridItem>
       </Grid>

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

@@ -0,0 +1,59 @@
+import type { SystemStyleObject } from '@invoke-ai/ui-library';
+import { Box, Flex } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { useCanvasEntityListDnd } from 'features/controlLayers/components/CanvasEntityList/useCanvasEntityListDnd';
+import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
+import { useEntityIsSelected } from 'features/controlLayers/hooks/useEntityIsSelected';
+import { entitySelected } from 'features/controlLayers/store/canvasSlice';
+import { DndListDropIndicator } from 'features/dnd/DndListDropIndicator';
+import type { PropsWithChildren } from 'react';
+import { memo, useCallback, useRef } from 'react';
+
+const sx = {
+  position: 'relative',
+  flexDir: 'column',
+  w: 'full',
+  bg: 'base.850',
+  borderRadius: 'base',
+  '&[data-selected=true]': {
+    bg: 'base.800',
+  },
+  '&[data-is-dragging=true]': {
+    opacity: 0.3,
+  },
+  transitionProperty: 'common',
+} satisfies SystemStyleObject;
+
+export const CanvasEntityContainer = memo((props: PropsWithChildren) => {
+  const dispatch = useAppDispatch();
+  const entityIdentifier = useEntityIdentifierContext();
+  const isSelected = useEntityIsSelected(entityIdentifier);
+  const onClick = useCallback(() => {
+    if (isSelected) {
+      return;
+    }
+    dispatch(entitySelected({ entityIdentifier }));
+  }, [dispatch, entityIdentifier, isSelected]);
+  const ref = useRef<HTMLDivElement>(null);
+
+  const [dndListState, isDragging] = useCanvasEntityListDnd(ref, entityIdentifier);
+
+  return (
+    <Box position="relative">
+      <Flex
+        // This is used to trigger the post-move flash animation
+        data-entity-id={entityIdentifier.id}
+        data-selected={isSelected}
+        data-is-dragging={isDragging}
+        ref={ref}
+        onClick={onClick}
+        sx={sx}
+      >
+        {props.children}
+      </Flex>
+      <DndListDropIndicator dndState={dndListState} />
+    </Box>
+  );
+});
+
+CanvasEntityContainer.displayName = 'CanvasEntityContainer';

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

@@ -0,0 +1,181 @@
+import { monitorForElements } from '@atlaskit/pragmatic-drag-and-drop/element/adapter';
+import { extractClosestEdge } from '@atlaskit/pragmatic-drag-and-drop-hitbox/closest-edge';
+import { reorderWithEdge } from '@atlaskit/pragmatic-drag-and-drop-hitbox/util/reorder-with-edge';
+import { Button, Collapse, Flex, Icon, Spacer, Text } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import { useBoolean } from 'common/hooks/useBoolean';
+import { colorTokenToCssVar } from 'common/util/colorTokenToCssVar';
+import { fixTooltipCloseOnScrollStyles } from 'common/util/fixTooltipCloseOnScrollStyles';
+import { CanvasEntityAddOfTypeButton } from 'features/controlLayers/components/common/CanvasEntityAddOfTypeButton';
+import { CanvasEntityMergeVisibleButton } from 'features/controlLayers/components/common/CanvasEntityMergeVisibleButton';
+import { CanvasEntityTypeIsHiddenToggle } from 'features/controlLayers/components/common/CanvasEntityTypeIsHiddenToggle';
+import { useEntityTypeInformationalPopover } from 'features/controlLayers/hooks/useEntityTypeInformationalPopover';
+import { useEntityTypeTitle } from 'features/controlLayers/hooks/useEntityTypeTitle';
+import { entitiesReordered } from 'features/controlLayers/store/canvasSlice';
+import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
+import { isRenderableEntityType } from 'features/controlLayers/store/types';
+import { singleCanvasEntityDndSource } from 'features/dnd/dnd';
+import { triggerPostMoveFlash } from 'features/dnd/util';
+import type { PropsWithChildren } from 'react';
+import { memo, useEffect } from 'react';
+import { flushSync } from 'react-dom';
+import { PiCaretDownBold } from 'react-icons/pi';
+
+type Props = PropsWithChildren<{
+  isSelected: boolean;
+  type: CanvasEntityIdentifier['type'];
+  entityIdentifiers: CanvasEntityIdentifier[];
+}>;
+
+export const CanvasEntityGroupList = memo(({ isSelected, type, children, entityIdentifiers }: Props) => {
+  const title = useEntityTypeTitle(type);
+  const informationalPopoverFeature = useEntityTypeInformationalPopover(type);
+  const collapse = useBoolean(true);
+  const dispatch = useAppDispatch();
+
+  useEffect(() => {
+    return monitorForElements({
+      canMonitor({ source }) {
+        if (!singleCanvasEntityDndSource.typeGuard(source.data)) {
+          return false;
+        }
+        if (source.data.payload.entityIdentifier.type !== type) {
+          return false;
+        }
+        return true;
+      },
+      onDrop({ location, source }) {
+        const target = location.current.dropTargets[0];
+        if (!target) {
+          return;
+        }
+
+        const sourceData = source.data;
+        const targetData = target.data;
+
+        if (!singleCanvasEntityDndSource.typeGuard(sourceData) || !singleCanvasEntityDndSource.typeGuard(targetData)) {
+          return;
+        }
+
+        const indexOfSource = entityIdentifiers.findIndex(
+          (entityIdentifier) => entityIdentifier.id === sourceData.payload.entityIdentifier.id
+        );
+        const indexOfTarget = entityIdentifiers.findIndex(
+          (entityIdentifier) => entityIdentifier.id === targetData.payload.entityIdentifier.id
+        );
+
+        if (indexOfTarget < 0 || indexOfSource < 0) {
+          return;
+        }
+
+        // Don't move if the source and target are the same index, meaning same position in the list
+        if (indexOfSource === indexOfTarget) {
+          return;
+        }
+
+        const closestEdgeOfTarget = extractClosestEdge(targetData);
+
+        // It's possible that the indices are different, but refer to the same position. For example, if the source is
+        // at 2 and the target is at 3, but the target edge is 'top', then the entity is already in the correct position.
+        // We should bail if this is the case.
+        let edgeIndexDelta = 0;
+
+        if (closestEdgeOfTarget === 'bottom') {
+          edgeIndexDelta = 1;
+        } else if (closestEdgeOfTarget === 'top') {
+          edgeIndexDelta = -1;
+        }
+
+        // If the source is already in the correct position, we don't need to move it.
+        if (indexOfSource === indexOfTarget + edgeIndexDelta) {
+          return;
+        }
+
+        // Using `flushSync` so we can query the DOM straight after this line
+        flushSync(() => {
+          dispatch(
+            entitiesReordered({
+              type,
+              entityIdentifiers: reorderWithEdge({
+                list: entityIdentifiers,
+                startIndex: indexOfSource,
+                indexOfTarget,
+                closestEdgeOfTarget,
+                axis: 'vertical',
+              }),
+            })
+          );
+        });
+
+        // Flash the element that was moved
+        const element = document.querySelector(`[data-entity-id="${sourceData.payload.entityIdentifier.id}"]`);
+        if (element instanceof HTMLElement) {
+          triggerPostMoveFlash(element, colorTokenToCssVar('base.700'));
+        }
+      },
+    });
+  }, [dispatch, entityIdentifiers, type]);
+
+  return (
+    <Flex flexDir="column" w="full">
+      <Flex w="full">
+        <Flex
+          flexGrow={1}
+          as={Button}
+          onClick={collapse.toggle}
+          justifyContent="space-between"
+          alignItems="center"
+          gap={3}
+          variant="unstyled"
+          p={0}
+          h={8}
+        >
+          <Icon
+            boxSize={4}
+            as={PiCaretDownBold}
+            transform={collapse.isTrue ? undefined : 'rotate(-90deg)'}
+            fill={isSelected ? 'base.200' : 'base.500'}
+            transitionProperty="common"
+            transitionDuration="fast"
+          />
+          {informationalPopoverFeature ? (
+            <InformationalPopover feature={informationalPopoverFeature}>
+              <Text
+                fontWeight="semibold"
+                color={isSelected ? 'base.200' : 'base.500'}
+                userSelect="none"
+                transitionProperty="common"
+                transitionDuration="fast"
+              >
+                {title}
+              </Text>
+            </InformationalPopover>
+          ) : (
+            <Text
+              fontWeight="semibold"
+              color={isSelected ? 'base.200' : 'base.500'}
+              userSelect="none"
+              transitionProperty="common"
+              transitionDuration="fast"
+            >
+              {title}
+            </Text>
+          )}
+
+          <Spacer />
+        </Flex>
+        {isRenderableEntityType(type) && <CanvasEntityMergeVisibleButton type={type} />}
+        {isRenderableEntityType(type) && <CanvasEntityTypeIsHiddenToggle type={type} />}
+        <CanvasEntityAddOfTypeButton type={type} />
+      </Flex>
+      <Collapse in={collapse.isTrue} style={fixTooltipCloseOnScrollStyles}>
+        <Flex flexDir="column" gap={2} pt={2}>
+          {children}
+        </Flex>
+      </Collapse>
+    </Flex>
+  );
+});
+
+CanvasEntityGroupList.displayName = 'CanvasEntityGroupList';

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/useCanvasEntityListDnd.ts

@@ -0,0 +1,83 @@
+import { combine } from '@atlaskit/pragmatic-drag-and-drop/combine';
+import { draggable, dropTargetForElements } from '@atlaskit/pragmatic-drag-and-drop/element/adapter';
+import { attachClosestEdge, extractClosestEdge } from '@atlaskit/pragmatic-drag-and-drop-hitbox/closest-edge';
+import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
+import { singleCanvasEntityDndSource } from 'features/dnd/dnd';
+import { type DndListTargetState, idle } from 'features/dnd/types';
+import type { RefObject } from 'react';
+import { useEffect, useState } from 'react';
+
+export const useCanvasEntityListDnd = (ref: RefObject<HTMLElement>, entityIdentifier: CanvasEntityIdentifier) => {
+  const [dndListState, setDndListState] = useState<DndListTargetState>(idle);
+  const [isDragging, setIsDragging] = useState(false);
+
+  useEffect(() => {
+    const element = ref.current;
+    if (!element) {
+      return;
+    }
+    return combine(
+      draggable({
+        element,
+        getInitialData() {
+          return singleCanvasEntityDndSource.getData({ entityIdentifier });
+        },
+        onDragStart() {
+          setDndListState({ type: 'is-dragging' });
+          setIsDragging(true);
+        },
+        onDrop() {
+          setDndListState(idle);
+          setIsDragging(false);
+        },
+      }),
+      dropTargetForElements({
+        element,
+        canDrop({ source }) {
+          if (!singleCanvasEntityDndSource.typeGuard(source.data)) {
+            return false;
+          }
+          if (source.data.payload.entityIdentifier.type !== entityIdentifier.type) {
+            return false;
+          }
+          return true;
+        },
+        getData({ input }) {
+          const data = singleCanvasEntityDndSource.getData({ entityIdentifier });
+          return attachClosestEdge(data, {
+            element,
+            input,
+            allowedEdges: ['top', 'bottom'],
+          });
+        },
+        getIsSticky() {
+          return true;
+        },
+        onDragEnter({ self }) {
+          const closestEdge = extractClosestEdge(self.data);
+          setDndListState({ type: 'is-dragging-over', closestEdge });
+        },
+        onDrag({ self }) {
+          const closestEdge = extractClosestEdge(self.data);
+
+          // Only need to update react state if nothing has changed.
+          // Prevents re-rendering.
+          setDndListState((current) => {
+            if (current.type === 'is-dragging-over' && current.closestEdge === closestEdge) {
+              return current;
+            }
+            return { type: 'is-dragging-over', closestEdge };
+          });
+        },
+        onDragLeave() {
+          setDndListState(idle);
+        },
+        onDrop() {
+          setDndListState(idle);
+        },
+      })
+    );
+  }, [entityIdentifier, ref]);
+
+  return [dndListState, isDragging] as const;
+};

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

@@ -7,6 +7,8 @@ import { EntityListSelectedEntityActionBar } from 'features/controlLayers/compon
 import { selectHasEntities } from 'features/controlLayers/store/selectors';
 import { memo, useRef } from 'react';
 
+import { ParamDenoisingStrength } from './ParamDenoisingStrength';
+
 export const CanvasLayersPanelContent = memo(() => {
   const hasEntities = useAppSelector(selectHasEntities);
   const layersPanelFocusRef = useRef<HTMLDivElement>(null);
@@ -16,6 +18,8 @@ export const CanvasLayersPanelContent = memo(() => {
     <Flex ref={layersPanelFocusRef} flexDir="column" gap={2} w="full" h="full">
       <EntityListSelectedEntityActionBar />
       <Divider py={0} />
+      <ParamDenoisingStrength />
+      <Divider py={0} />
       {!hasEntities && <CanvasAddEntityButtons />}
       {hasEntities && <CanvasEntityList />}
     </Flex>

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

@@ -109,7 +109,9 @@ export const CanvasMainPanelContent = memo(() => {
           <SelectObject />
         </CanvasManagerProviderGate>
       </Flex>
-      <CanvasDropArea />
+      <CanvasManagerProviderGate>
+        <CanvasDropArea />
+      </CanvasManagerProviderGate>
       <GatedImageViewer />
     </Flex>
   );

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

@@ -1,16 +1,20 @@
-import { useDndContext } from '@dnd-kit/core';
+import { combine } from '@atlaskit/pragmatic-drag-and-drop/combine';
+import { dropTargetForElements, monitorForElements } from '@atlaskit/pragmatic-drag-and-drop/element/adapter';
+import { dropTargetForExternal, monitorForExternal } from '@atlaskit/pragmatic-drag-and-drop/external/adapter';
 import { Box, Button, Spacer, Tab, TabList, TabPanel, TabPanels, Tabs } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import IAIDropOverlay from 'common/components/IAIDropOverlay';
+import { useAppDispatch, useAppSelector, useAppStore } from 'app/store/storeHooks';
 import { CanvasLayersPanelContent } from 'features/controlLayers/components/CanvasLayersPanelContent';
 import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { selectEntityCountActive } from 'features/controlLayers/store/selectors';
+import { multipleImageDndSource, singleImageDndSource } from 'features/dnd/dnd';
+import { DndDropOverlay } from 'features/dnd/DndDropOverlay';
+import type { DndTargetState } from 'features/dnd/types';
 import GalleryPanelContent from 'features/gallery/components/GalleryPanelContent';
 import { useImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
 import { selectActiveTabCanvasRightPanel } from 'features/ui/store/uiSelectors';
 import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
-import { memo, useCallback, useMemo, useRef, useState } from 'react';
+import { memo, useCallback, useEffect, useMemo, useRef, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 
 export const CanvasRightPanel = memo(() => {
@@ -79,37 +83,13 @@ CanvasRightPanel.displayName = 'CanvasRightPanel';
 
 const PanelTabs = memo(() => {
   const { t } = useTranslation();
-  const activeTab = useAppSelector(selectActiveTabCanvasRightPanel);
+  const store = useAppStore();
   const activeEntityCount = useAppSelector(selectEntityCountActive);
-  const tabTimeout = useRef<number | null>(null);
-  const dndCtx = useDndContext();
-  const dispatch = useAppDispatch();
-  const [mouseOverTab, setMouseOverTab] = useState<'layers' | 'gallery' | null>(null);
-
-  const onOnMouseOverLayersTab = useCallback(() => {
-    setMouseOverTab('layers');
-    tabTimeout.current = window.setTimeout(() => {
-      if (dndCtx.active) {
-        dispatch(activeTabCanvasRightPanelChanged('layers'));
-      }
-    }, 300);
-  }, [dndCtx.active, dispatch]);
-
-  const onOnMouseOverGalleryTab = useCallback(() => {
-    setMouseOverTab('gallery');
-    tabTimeout.current = window.setTimeout(() => {
-      if (dndCtx.active) {
-        dispatch(activeTabCanvasRightPanelChanged('gallery'));
-      }
-    }, 300);
-  }, [dndCtx.active, dispatch]);
-
-  const onMouseOut = useCallback(() => {
-    setMouseOverTab(null);
-    if (tabTimeout.current) {
-      clearTimeout(tabTimeout.current);
-    }
-  }, []);
+  const [layersTabDndState, setLayersTabDndState] = useState<DndTargetState>('idle');
+  const [galleryTabDndState, setGalleryTabDndState] = useState<DndTargetState>('idle');
+  const layersTabRef = useRef<HTMLDivElement>(null);
+  const galleryTabRef = useRef<HTMLDivElement>(null);
+  const timeoutRef = useRef<number | null>(null);
 
   const layersTabLabel = useMemo(() => {
     if (activeEntityCount === 0) {
@@ -118,23 +98,172 @@ const PanelTabs = memo(() => {
     return `${t('controlLayers.layer_other')} (${activeEntityCount})`;
   }, [activeEntityCount, t]);
 
+  useEffect(() => {
+    if (!layersTabRef.current) {
+      return;
+    }
+
+    const getIsOnLayersTab = () => selectActiveTabCanvasRightPanel(store.getState()) === 'layers';
+
+    const onDragEnter = () => {
+      // If we are already on the layers tab, do nothing
+      if (getIsOnLayersTab()) {
+        return;
+      }
+
+      // Else set the state to active and switch to the layers tab after a timeout
+      setLayersTabDndState('over');
+      timeoutRef.current = window.setTimeout(() => {
+        timeoutRef.current = null;
+        store.dispatch(activeTabCanvasRightPanelChanged('layers'));
+        // When we switch tabs, the other tab should be pending
+        setLayersTabDndState('idle');
+        setGalleryTabDndState('potential');
+      }, 300);
+    };
+    const onDragLeave = () => {
+      // Set the state to idle or pending depending on the current tab
+      if (getIsOnLayersTab()) {
+        setLayersTabDndState('idle');
+      } else {
+        setLayersTabDndState('potential');
+      }
+      // Abort the tab switch if it hasn't happened yet
+      if (timeoutRef.current !== null) {
+        clearTimeout(timeoutRef.current);
+      }
+    };
+    const onDragStart = () => {
+      // Set the state to pending when a drag starts
+      setLayersTabDndState('potential');
+    };
+    return combine(
+      dropTargetForElements({
+        element: layersTabRef.current,
+        onDragEnter,
+        onDragLeave,
+      }),
+      monitorForElements({
+        canMonitor: ({ source }) => {
+          if (!singleImageDndSource.typeGuard(source.data) && !multipleImageDndSource.typeGuard(source.data)) {
+            return false;
+          }
+          // Only monitor if we are not already on the gallery tab
+          return !getIsOnLayersTab();
+        },
+        onDragStart,
+      }),
+      dropTargetForExternal({
+        element: layersTabRef.current,
+        onDragEnter,
+        onDragLeave,
+      }),
+      monitorForExternal({
+        canMonitor: () => !getIsOnLayersTab(),
+        onDragStart,
+      })
+    );
+  }, [store]);
+
+  useEffect(() => {
+    if (!galleryTabRef.current) {
+      return;
+    }
+
+    const getIsOnGalleryTab = () => selectActiveTabCanvasRightPanel(store.getState()) === 'gallery';
+
+    const onDragEnter = () => {
+      // If we are already on the gallery tab, do nothing
+      if (getIsOnGalleryTab()) {
+        return;
+      }
+
+      // Else set the state to active and switch to the gallery tab after a timeout
+      setGalleryTabDndState('over');
+      timeoutRef.current = window.setTimeout(() => {
+        timeoutRef.current = null;
+        store.dispatch(activeTabCanvasRightPanelChanged('gallery'));
+        // When we switch tabs, the other tab should be pending
+        setGalleryTabDndState('idle');
+        setLayersTabDndState('potential');
+      }, 300);
+    };
+
+    const onDragLeave = () => {
+      // Set the state to idle or pending depending on the current tab
+      if (getIsOnGalleryTab()) {
+        setGalleryTabDndState('idle');
+      } else {
+        setGalleryTabDndState('potential');
+      }
+      // Abort the tab switch if it hasn't happened yet
+      if (timeoutRef.current !== null) {
+        clearTimeout(timeoutRef.current);
+      }
+    };
+
+    const onDragStart = () => {
+      // Set the state to pending when a drag starts
+      setGalleryTabDndState('potential');
+    };
+
+    return combine(
+      dropTargetForElements({
+        element: galleryTabRef.current,
+        onDragEnter,
+        onDragLeave,
+      }),
+      monitorForElements({
+        canMonitor: ({ source }) => {
+          if (!singleImageDndSource.typeGuard(source.data) && !multipleImageDndSource.typeGuard(source.data)) {
+            return false;
+          }
+          // Only monitor if we are not already on the gallery tab
+          return !getIsOnGalleryTab();
+        },
+        onDragStart,
+      }),
+      dropTargetForExternal({
+        element: galleryTabRef.current,
+        onDragEnter,
+        onDragLeave,
+      }),
+      monitorForExternal({
+        canMonitor: () => !getIsOnGalleryTab(),
+        onDragStart,
+      })
+    );
+  }, [store]);
+
+  useEffect(() => {
+    const onDrop = () => {
+      // Reset the dnd state when a drop happens
+      setGalleryTabDndState('idle');
+      setLayersTabDndState('idle');
+    };
+    const cleanup = combine(monitorForElements({ onDrop }), monitorForExternal({ onDrop }));
+
+    return () => {
+      cleanup();
+      if (timeoutRef.current !== null) {
+        clearTimeout(timeoutRef.current);
+      }
+    };
+  }, []);
+
   return (
     <>
-      <Tab position="relative" onMouseOver={onOnMouseOverLayersTab} onMouseOut={onMouseOut} w={32}>
+      <Tab ref={layersTabRef} position="relative" w={32}>
         <Box as="span" w="full">
           {layersTabLabel}
         </Box>
-        {dndCtx.active && activeTab !== 'layers' && (
-          <IAIDropOverlay isOver={mouseOverTab === 'layers'} withBackdrop={false} />
-        )}
+        <DndDropOverlay dndState={layersTabDndState} withBackdrop={false} />
       </Tab>
-      <Tab position="relative" onMouseOver={onOnMouseOverGalleryTab} onMouseOut={onMouseOut} w={32}>
+      <Tab ref={galleryTabRef} position="relative" w={32}>
         <Box as="span" w="full">
           {t('gallery.gallery')}
         </Box>
-        {dndCtx.active && activeTab !== 'gallery' && (
-          <IAIDropOverlay isOver={mouseOverTab === 'gallery'} withBackdrop={false} />
-        )}
+        <DndDropOverlay dndState={galleryTabDndState} withBackdrop={false} />
       </Tab>
     </>
   );

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

@@ -1,17 +1,19 @@
 import { Spacer } from '@invoke-ai/ui-library';
-import IAIDroppable from 'common/components/IAIDroppable';
-import { CanvasEntityContainer } from 'features/controlLayers/components/common/CanvasEntityContainer';
+import { CanvasEntityContainer } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityContainer';
 import { CanvasEntityHeader } from 'features/controlLayers/components/common/CanvasEntityHeader';
 import { CanvasEntityHeaderCommonActions } from 'features/controlLayers/components/common/CanvasEntityHeaderCommonActions';
 import { CanvasEntityPreviewImage } from 'features/controlLayers/components/common/CanvasEntityPreviewImage';
 import { CanvasEntitySettingsWrapper } from 'features/controlLayers/components/common/CanvasEntitySettingsWrapper';
 import { CanvasEntityEditableTitle } from 'features/controlLayers/components/common/CanvasEntityTitleEdit';
 import { ControlLayerBadges } from 'features/controlLayers/components/ControlLayer/ControlLayerBadges';
-import { ControlLayerControlAdapter } from 'features/controlLayers/components/ControlLayer/ControlLayerControlAdapter';
+import { ControlLayerSettings } from 'features/controlLayers/components/ControlLayer/ControlLayerSettings';
 import { ControlLayerAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { EntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
+import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
-import type { ReplaceLayerImageDropData } from 'features/dnd/types';
+import type { ReplaceCanvasEntityObjectsWithImageDndTargetData } from 'features/dnd/dnd';
+import { replaceCanvasEntityObjectsWithImageDndTarget } from 'features/dnd/dnd';
+import { DndDropTarget } from 'features/dnd/DndDropTarget';
 import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 
@@ -21,14 +23,16 @@ type Props = {
 
 export const ControlLayer = memo(({ id }: Props) => {
   const { t } = useTranslation();
+  const isBusy = useCanvasIsBusy();
   const entityIdentifier = useMemo<CanvasEntityIdentifier<'control_layer'>>(
     () => ({ id, type: 'control_layer' }),
     [id]
   );
-  const dropData = useMemo<ReplaceLayerImageDropData>(
-    () => ({ id, actionType: 'REPLACE_LAYER_WITH_IMAGE', context: { entityIdentifier } }),
-    [id, entityIdentifier]
+  const dndTargetData = useMemo<ReplaceCanvasEntityObjectsWithImageDndTargetData>(
+    () => replaceCanvasEntityObjectsWithImageDndTarget.getData({ entityIdentifier }, entityIdentifier.id),
+    [entityIdentifier]
   );
+
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>
       <ControlLayerAdapterGate>
@@ -41,9 +45,14 @@ export const ControlLayer = memo(({ id }: Props) => {
             <CanvasEntityHeaderCommonActions />
           </CanvasEntityHeader>
           <CanvasEntitySettingsWrapper>
-            <ControlLayerControlAdapter />
+            <ControlLayerSettings />
           </CanvasEntitySettingsWrapper>
-          <IAIDroppable data={dropData} dropLabel={t('controlLayers.replaceLayer')} />
+          <DndDropTarget
+            dndTarget={replaceCanvasEntityObjectsWithImageDndTarget}
+            dndTargetData={dndTargetData}
+            label={t('controlLayers.replaceLayer')}
+            isDisabled={isBusy}
+          />
         </CanvasEntityContainer>
       </ControlLayerAdapterGate>
     </EntityIdentifierContext.Provider>

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

@@ -1,11 +1,13 @@
 import { Flex, IconButton } from '@invoke-ai/ui-library';
 import { createMemoizedAppSelector } from 'app/store/createMemoizedSelector';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAppStore } from 'app/store/nanostores/store';
+import { useAppSelector } from 'app/store/storeHooks';
 import { useImageUploadButton } from 'common/hooks/useImageUploadButton';
 import { BeginEndStepPct } from 'features/controlLayers/components/common/BeginEndStepPct';
 import { Weight } from 'features/controlLayers/components/common/Weight';
 import { ControlLayerControlAdapterControlMode } from 'features/controlLayers/components/ControlLayer/ControlLayerControlAdapterControlMode';
 import { ControlLayerControlAdapterModel } from 'features/controlLayers/components/ControlLayer/ControlLayerControlAdapterModel';
+import { useEntityAdapterContext } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
 import { usePullBboxIntoLayer } from 'features/controlLayers/hooks/saveCanvasHooks';
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
@@ -16,13 +18,15 @@ import {
   controlLayerModelChanged,
   controlLayerWeightChanged,
 } from 'features/controlLayers/store/canvasSlice';
+import { getFilterForModel } from 'features/controlLayers/store/filters';
 import { selectIsFLUX } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice, selectEntityOrThrow } from 'features/controlLayers/store/selectors';
 import type { CanvasEntityIdentifier, ControlModeV2 } from 'features/controlLayers/store/types';
+import { replaceCanvasEntityObjectsWithImage } from 'features/imageActions/actions';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiBoundingBoxBold, PiShootingStarFill, PiUploadBold } from 'react-icons/pi';
-import type { ControlNetModelConfig, PostUploadAction, T2IAdapterModelConfig } from 'services/api/types';
+import type { ControlNetModelConfig, ImageDTO, T2IAdapterModelConfig } from 'services/api/types';
 
 const useControlLayerControlAdapter = (entityIdentifier: CanvasEntityIdentifier<'control_layer'>) => {
   const selectControlAdapter = useMemo(
@@ -39,11 +43,12 @@ const useControlLayerControlAdapter = (entityIdentifier: CanvasEntityIdentifier<
 
 export const ControlLayerControlAdapter = memo(() => {
   const { t } = useTranslation();
-  const dispatch = useAppDispatch();
+  const { dispatch, getState } = useAppStore();
   const entityIdentifier = useEntityIdentifierContext('control_layer');
   const controlAdapter = useControlLayerControlAdapter(entityIdentifier);
   const filter = useEntityFilter(entityIdentifier);
   const isFLUX = useAppSelector(selectIsFLUX);
+  const adapter = useEntityAdapterContext('control_layer');
 
   const onChangeBeginEndStepPct = useCallback(
     (beginEndStepPct: [number, number]) => {
@@ -69,17 +74,58 @@ export const ControlLayerControlAdapter = memo(() => {
   const onChangeModel = useCallback(
     (modelConfig: ControlNetModelConfig | T2IAdapterModelConfig) => {
       dispatch(controlLayerModelChanged({ entityIdentifier, modelConfig }));
+      // When we change the model, we need may need to start filtering w/ the simplified filter mode, and/or change the
+      // filter config.
+      const isFiltering = adapter.filterer.$isFiltering.get();
+      const isSimple = adapter.filterer.$simple.get();
+      // If we are filtering and _not_ in simple mode, that means the user has clicked Advanced. They want to be in control
+      // of the settings. Bail early without doing anything else.
+      if (isFiltering && !isSimple) {
+        return;
+      }
+
+      // Else, we are in simple mode and will take care of some things for the user.
+
+      // First, check if the newly-selected model has a default filter. It may not - for example, Tile controlnet models
+      // don't have a default filter.
+      const defaultFilterForNewModel = getFilterForModel(modelConfig);
+
+      if (!defaultFilterForNewModel) {
+        // The user has chosen a model that doesn't have a default filter - cancel any in-progress filtering and bail.
+        if (isFiltering) {
+          adapter.filterer.cancel();
+        }
+        return;
+      }
+
+      // At this point, we know the user has selected a model that has a default filter. We need to either start filtering
+      // with that default filter, or update the existing filter config to match the new model's default filter.
+      const filterConfig = defaultFilterForNewModel.buildDefaults();
+      if (isFiltering) {
+        adapter.filterer.$filterConfig.set(filterConfig);
+      } else {
+        adapter.filterer.start(filterConfig);
+      }
+      // The user may have disabled auto-processing, so we should process the filter manually. This is essentially a
+      // no-op if auto-processing is already enabled, because the process method is debounced.
+      adapter.filterer.process();
     },
-    [dispatch, entityIdentifier]
+    [adapter.filterer, dispatch, entityIdentifier]
   );
 
   const pullBboxIntoLayer = usePullBboxIntoLayer(entityIdentifier);
   const isBusy = useCanvasIsBusy();
-  const postUploadAction = useMemo<PostUploadAction>(
-    () => ({ type: 'REPLACE_LAYER_WITH_IMAGE', entityIdentifier }),
-    [entityIdentifier]
+  const uploadOptions = useMemo(
+    () =>
+      ({
+        onUpload: (imageDTO: ImageDTO) => {
+          replaceCanvasEntityObjectsWithImage({ entityIdentifier, imageDTO, dispatch, getState });
+        },
+        allowMultiple: false,
+      }) as const,
+    [dispatch, entityIdentifier, getState]
   );
-  const uploadApi = useImageUploadButton({ postUploadAction });
+  const uploadApi = useImageUploadButton(uploadOptions);
 
   return (
     <Flex flexDir="column" gap={3} position="relative" w="full">

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

@@ -1,14 +1,14 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { CanvasEntityGroupList } from 'features/controlLayers/components/common/CanvasEntityGroupList';
+import { CanvasEntityGroupList } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityGroupList';
 import { ControlLayer } from 'features/controlLayers/components/ControlLayer/ControlLayer';
-import { mapId } from 'features/controlLayers/konva/util';
 import { selectCanvasSlice, selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
+import { getEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo } from 'react';
 
-const selectEntityIds = createMemoizedSelector(selectCanvasSlice, (canvas) => {
-  return canvas.controlLayers.entities.map(mapId).reverse();
+const selectEntityIdentifiers = createMemoizedSelector(selectCanvasSlice, (canvas) => {
+  return canvas.controlLayers.entities.map(getEntityIdentifier).toReversed();
 });
 
 const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selectedEntityIdentifier) => {
@@ -17,17 +17,17 @@ const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selecte
 
 export const ControlLayerEntityList = memo(() => {
   const isSelected = useAppSelector(selectIsSelected);
-  const layerIds = useAppSelector(selectEntityIds);
+  const entityIdentifiers = useAppSelector(selectEntityIdentifiers);
 
-  if (layerIds.length === 0) {
+  if (entityIdentifiers.length === 0) {
     return null;
   }
 
-  if (layerIds.length > 0) {
+  if (entityIdentifiers.length > 0) {
     return (
-      <CanvasEntityGroupList type="control_layer" isSelected={isSelected}>
-        {layerIds.map((id) => (
-          <ControlLayer key={id} id={id} />
+      <CanvasEntityGroupList type="control_layer" isSelected={isSelected} entityIdentifiers={entityIdentifiers}>
+        {entityIdentifiers.map((entityIdentifier) => (
+          <ControlLayer key={entityIdentifier.id} id={entityIdentifier.id} />
         ))}
       </CanvasEntityGroupList>
     );

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

@@ -0,0 +1,18 @@
+import { ControlLayerControlAdapter } from 'features/controlLayers/components/ControlLayer/ControlLayerControlAdapter';
+import { ControlLayerSettingsEmptyState } from 'features/controlLayers/components/ControlLayer/ControlLayerSettingsEmptyState';
+import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
+import { useEntityIsEmpty } from 'features/controlLayers/hooks/useEntityIsEmpty';
+import { memo } from 'react';
+
+export const ControlLayerSettings = memo(() => {
+  const entityIdentifier = useEntityIdentifierContext();
+  const isEmpty = useEntityIsEmpty(entityIdentifier);
+
+  if (isEmpty) {
+    return <ControlLayerSettingsEmptyState />;
+  }
+
+  return <ControlLayerControlAdapter />;
+});
+
+ControlLayerSettings.displayName = 'ControlLayerSettings';

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

@@ -0,0 +1,53 @@
+import { Button, Flex, Text } from '@invoke-ai/ui-library';
+import { useAppStore } from 'app/store/nanostores/store';
+import { useImageUploadButton } from 'common/hooks/useImageUploadButton';
+import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
+import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
+import { replaceCanvasEntityObjectsWithImage } from 'features/imageActions/actions';
+import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
+import { memo, useCallback } from 'react';
+import { Trans } from 'react-i18next';
+import type { ImageDTO } from 'services/api/types';
+
+export const ControlLayerSettingsEmptyState = memo(() => {
+  const entityIdentifier = useEntityIdentifierContext('control_layer');
+  const { dispatch, getState } = useAppStore();
+  const isBusy = useCanvasIsBusy();
+  const onUpload = useCallback(
+    (imageDTO: ImageDTO) => {
+      replaceCanvasEntityObjectsWithImage({ imageDTO, entityIdentifier, dispatch, getState });
+    },
+    [dispatch, entityIdentifier, getState]
+  );
+  const uploadApi = useImageUploadButton({ onUpload, allowMultiple: false });
+  const onClickGalleryButton = useCallback(() => {
+    dispatch(activeTabCanvasRightPanelChanged('gallery'));
+  }, [dispatch]);
+
+  return (
+    <Flex flexDir="column" gap={3} position="relative" w="full" p={4}>
+      <Text textAlign="center" color="base.300">
+        <Trans
+          i18nKey="controlLayers.controlLayerEmptyState"
+          components={{
+            UploadButton: (
+              <Button
+                isDisabled={isBusy}
+                size="sm"
+                variant="link"
+                color="base.300"
+                {...uploadApi.getUploadButtonProps()}
+              />
+            ),
+            GalleryButton: (
+              <Button onClick={onClickGalleryButton} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
+            ),
+          }}
+        />
+      </Text>
+      <input {...uploadApi.getUploadInputProps()} />
+    </Flex>
+  );
+});
+
+ControlLayerSettingsEmptyState.displayName = 'ControlLayerSettingsEmptyState';

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

@@ -9,6 +9,7 @@ import {
   MenuList,
   Spacer,
   Spinner,
+  Text,
 } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useAppSelector } from 'app/store/storeHooks';
@@ -28,13 +29,10 @@ import { memo, useCallback, useMemo, useRef } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiCaretDownBold } from 'react-icons/pi';
 
-const FilterContent = memo(
+const FilterContentAdvanced = memo(
   ({ adapter }: { adapter: CanvasEntityAdapterRasterLayer | CanvasEntityAdapterControlLayer }) => {
     const { t } = useTranslation();
-    const ref = useRef<HTMLDivElement>(null);
-    useFocusRegion('canvas', ref, { focusOnMount: true });
     const config = useStore(adapter.filterer.$filterConfig);
-    const isCanvasFocused = useIsRegionFocused('canvas');
     const isProcessing = useStore(adapter.filterer.$isProcessing);
     const hasImageState = useStore(adapter.filterer.$hasImageState);
     const autoProcess = useAppSelector(selectAutoProcess);
@@ -73,36 +71,8 @@ const FilterContent = memo(
       adapter.filterer.saveAs('control_layer');
     }, [adapter.filterer]);
 
-    useRegisteredHotkeys({
-      id: 'applyFilter',
-      category: 'canvas',
-      callback: adapter.filterer.apply,
-      options: { enabled: !isProcessing && isCanvasFocused },
-      dependencies: [adapter.filterer, isProcessing, isCanvasFocused],
-    });
-
-    useRegisteredHotkeys({
-      id: 'cancelFilter',
-      category: 'canvas',
-      callback: adapter.filterer.cancel,
-      options: { enabled: !isProcessing && isCanvasFocused },
-      dependencies: [adapter.filterer, isProcessing, isCanvasFocused],
-    });
-
     return (
-      <Flex
-        ref={ref}
-        bg="base.800"
-        borderRadius="base"
-        p={4}
-        flexDir="column"
-        gap={4}
-        w={420}
-        h="auto"
-        shadow="dark-lg"
-        transitionProperty="height"
-        transitionDuration="normal"
-      >
+      <>
         <Flex w="full" gap={4}>
           <Heading size="md" color="base.300" userSelect="none">
             {t('controlLayers.filter.filter')}
@@ -169,12 +139,67 @@ const FilterContent = memo(
             {t('controlLayers.filter.cancel')}
           </Button>
         </ButtonGroup>
-      </Flex>
+      </>
     );
   }
 );
 
-FilterContent.displayName = 'FilterContent';
+FilterContentAdvanced.displayName = 'FilterContentAdvanced';
+
+const FilterContentSimple = memo(
+  ({ adapter }: { adapter: CanvasEntityAdapterRasterLayer | CanvasEntityAdapterControlLayer }) => {
+    const { t } = useTranslation();
+    const config = useStore(adapter.filterer.$filterConfig);
+    const isProcessing = useStore(adapter.filterer.$isProcessing);
+    const hasImageState = useStore(adapter.filterer.$hasImageState);
+
+    const isValid = useMemo(() => {
+      return IMAGE_FILTERS[config.type].validateConfig?.(config as never) ?? true;
+    }, [config]);
+
+    const onClickAdvanced = useCallback(() => {
+      adapter.filterer.$simple.set(false);
+    }, [adapter.filterer.$simple]);
+
+    return (
+      <>
+        <Flex w="full" gap={4}>
+          <Heading size="md" color="base.300" userSelect="none">
+            {t('controlLayers.filter.filter')}
+          </Heading>
+          <Spacer />
+        </Flex>
+        <Flex flexDir="column" w="full" gap={2} pb={2}>
+          <Text color="base.500" textAlign="center">
+            {t('controlLayers.filter.processingLayerWith', { type: t(`controlLayers.filter.${config.type}.label`) })}
+          </Text>
+          <Text color="base.500" textAlign="center">
+            {t('controlLayers.filter.forMoreControl')}
+          </Text>
+        </Flex>
+        <ButtonGroup isAttached={false} size="sm" w="full">
+          <Button variant="ghost" onClick={onClickAdvanced}>
+            {t('controlLayers.filter.advanced')}
+          </Button>
+          <Spacer />
+          <Button
+            onClick={adapter.filterer.apply}
+            loadingText={t('controlLayers.filter.apply')}
+            variant="ghost"
+            isDisabled={isProcessing || !isValid || !hasImageState}
+          >
+            {t('controlLayers.filter.apply')}
+          </Button>
+          <Button variant="ghost" onClick={adapter.filterer.cancel} loadingText={t('controlLayers.filter.cancel')}>
+            {t('controlLayers.filter.cancel')}
+          </Button>
+        </ButtonGroup>
+      </>
+    );
+  }
+);
+
+FilterContentSimple.displayName = 'FilterContentSimple';
 
 export const Filter = () => {
   const canvasManager = useCanvasManager();
@@ -182,8 +207,54 @@ export const Filter = () => {
   if (!adapter) {
     return null;
   }
-
   return <FilterContent adapter={adapter} />;
 };
 
 Filter.displayName = 'Filter';
+
+const FilterContent = memo(
+  ({ adapter }: { adapter: CanvasEntityAdapterRasterLayer | CanvasEntityAdapterControlLayer }) => {
+    const simplified = useStore(adapter.filterer.$simple);
+    const isCanvasFocused = useIsRegionFocused('canvas');
+    const isProcessing = useStore(adapter.filterer.$isProcessing);
+    const ref = useRef<HTMLDivElement>(null);
+    useFocusRegion('canvas', ref, { focusOnMount: true });
+
+    useRegisteredHotkeys({
+      id: 'applyFilter',
+      category: 'canvas',
+      callback: adapter.filterer.apply,
+      options: { enabled: !isProcessing && isCanvasFocused, enableOnFormTags: true },
+      dependencies: [adapter.filterer, isProcessing, isCanvasFocused],
+    });
+
+    useRegisteredHotkeys({
+      id: 'cancelFilter',
+      category: 'canvas',
+      callback: adapter.filterer.cancel,
+      options: { enabled: !isProcessing && isCanvasFocused, enableOnFormTags: true },
+      dependencies: [adapter.filterer, isProcessing, isCanvasFocused],
+    });
+
+    return (
+      <Flex
+        ref={ref}
+        bg="base.800"
+        borderRadius="base"
+        p={4}
+        flexDir="column"
+        gap={4}
+        w={420}
+        h="auto"
+        shadow="dark-lg"
+        transitionProperty="height"
+        transitionDuration="normal"
+      >
+        {simplified && <FilterContentSimple adapter={adapter} />}
+        {!simplified && <FilterContentAdvanced adapter={adapter} />}
+      </Flex>
+    );
+  }
+);
+
+FilterContent.displayName = 'FilterContent';

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

@@ -1,5 +1,5 @@
 import { Spacer } from '@invoke-ai/ui-library';
-import { CanvasEntityContainer } from 'features/controlLayers/components/common/CanvasEntityContainer';
+import { CanvasEntityContainer } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityContainer';
 import { CanvasEntityHeader } from 'features/controlLayers/components/common/CanvasEntityHeader';
 import { CanvasEntityHeaderCommonActions } from 'features/controlLayers/components/common/CanvasEntityHeaderCommonActions';
 import { CanvasEntityEditableTitle } from 'features/controlLayers/components/common/CanvasEntityTitleEdit';

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

@@ -1,82 +1,80 @@
 import { Flex } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { skipToken } from '@reduxjs/toolkit/query';
-import IAIDndImage from 'common/components/IAIDndImage';
-import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
-import { useNanoid } from 'common/hooks/useNanoid';
+import { UploadImageButton } from 'common/hooks/useImageUploadButton';
 import type { ImageWithDims } from 'features/controlLayers/store/types';
-import type { ImageDraggableData, TypesafeDroppableData } from 'features/dnd/types';
-import { memo, useCallback, useEffect, useMemo } from 'react';
+import type { setGlobalReferenceImageDndTarget, setRegionalGuidanceReferenceImageDndTarget } from 'features/dnd/dnd';
+import { DndDropTarget } from 'features/dnd/DndDropTarget';
+import { DndImage } from 'features/dnd/DndImage';
+import { DndImageIcon } from 'features/dnd/DndImageIcon';
+import { memo, useCallback, useEffect } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowCounterClockwiseBold } from 'react-icons/pi';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
-import type { ImageDTO, PostUploadAction } from 'services/api/types';
+import type { ImageDTO } from 'services/api/types';
 import { $isConnected } from 'services/events/stores';
 
-type Props = {
+type Props<T extends typeof setGlobalReferenceImageDndTarget | typeof setRegionalGuidanceReferenceImageDndTarget> = {
   image: ImageWithDims | null;
   onChangeImage: (imageDTO: ImageDTO | null) => void;
-  droppableData: TypesafeDroppableData;
-  postUploadAction: PostUploadAction;
+  dndTarget: T;
+  dndTargetData: ReturnType<T['getData']>;
 };
 
-export const IPAdapterImagePreview = memo(({ image, onChangeImage, droppableData, postUploadAction }: Props) => {
-  const { t } = useTranslation();
-  const isConnected = useStore($isConnected);
-  const dndId = useNanoid('ip_adapter_image_preview');
+export const IPAdapterImagePreview = memo(
+  <T extends typeof setGlobalReferenceImageDndTarget | typeof setRegionalGuidanceReferenceImageDndTarget>({
+    image,
+    onChangeImage,
+    dndTarget,
+    dndTargetData,
+  }: Props<T>) => {
+    const { t } = useTranslation();
+    const isConnected = useStore($isConnected);
+    const { currentData: imageDTO, isError } = useGetImageDTOQuery(image?.image_name ?? skipToken);
+    const handleResetControlImage = useCallback(() => {
+      onChangeImage(null);
+    }, [onChangeImage]);
 
-  const { currentData: controlImage, isError: isErrorControlImage } = useGetImageDTOQuery(
-    image?.image_name ?? skipToken
-  );
-  const handleResetControlImage = useCallback(() => {
-    onChangeImage(null);
-  }, [onChangeImage]);
+    useEffect(() => {
+      if (isConnected && isError) {
+        handleResetControlImage();
+      }
+    }, [handleResetControlImage, isError, isConnected]);
 
-  const draggableData = useMemo<ImageDraggableData | undefined>(() => {
-    if (controlImage) {
-      return {
-        id: dndId,
-        payloadType: 'IMAGE_DTO',
-        payload: { imageDTO: controlImage },
-      };
-    }
-  }, [controlImage, dndId]);
+    const onUpload = useCallback(
+      (imageDTO: ImageDTO) => {
+        onChangeImage(imageDTO);
+      },
+      [onChangeImage]
+    );
 
-  useEffect(() => {
-    if (isConnected && isErrorControlImage) {
-      handleResetControlImage();
-    }
-  }, [handleResetControlImage, isConnected, isErrorControlImage]);
-
-  return (
-    <Flex
-      position="relative"
-      w="full"
-      h="full"
-      alignItems="center"
-      borderColor="error.500"
-      borderStyle="solid"
-      borderWidth={controlImage ? 0 : 1}
-      borderRadius="base"
-    >
-      <IAIDndImage
-        draggableData={draggableData}
-        droppableData={droppableData}
-        imageDTO={controlImage}
-        postUploadAction={postUploadAction}
-      />
-
-      {controlImage && (
-        <Flex position="absolute" flexDir="column" top={2} insetInlineEnd={2} gap={1}>
-          <IAIDndImageIcon
-            onClick={handleResetControlImage}
-            icon={<PiArrowCounterClockwiseBold size={16} />}
-            tooltip={t('common.reset')}
+    return (
+      <Flex position="relative" w="full" h="full" alignItems="center" data-error={!imageDTO && !image?.image_name}>
+        {!imageDTO && (
+          <UploadImageButton
+            w="full"
+            h="full"
+            isError={!imageDTO && !image?.image_name}
+            onUpload={onUpload}
+            fontSize={36}
           />
-        </Flex>
-      )}
-    </Flex>
-  );
-});
+        )}
+        {imageDTO && (
+          <>
+            <DndImage imageDTO={imageDTO} />
+            <Flex position="absolute" flexDir="column" top={2} insetInlineEnd={2} gap={1}>
+              <DndImageIcon
+                onClick={handleResetControlImage}
+                icon={<PiArrowCounterClockwiseBold size={16} />}
+                tooltip={t('common.reset')}
+              />
+            </Flex>
+          </>
+        )}
+        <DndDropTarget dndTarget={dndTarget} dndTargetData={dndTargetData} label={t('gallery.drop')} />
+      </Flex>
+    );
+  }
+);
 
 IPAdapterImagePreview.displayName = 'IPAdapterImagePreview';

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

@@ -2,14 +2,14 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { CanvasEntityGroupList } from 'features/controlLayers/components/common/CanvasEntityGroupList';
+import { CanvasEntityGroupList } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityGroupList';
 import { IPAdapter } from 'features/controlLayers/components/IPAdapter/IPAdapter';
-import { mapId } from 'features/controlLayers/konva/util';
 import { selectCanvasSlice, selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
+import { getEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo } from 'react';
 
-const selectEntityIds = createMemoizedSelector(selectCanvasSlice, (canvas) => {
-  return canvas.referenceImages.entities.map(mapId).reverse();
+const selectEntityIdentifiers = createMemoizedSelector(selectCanvasSlice, (canvas) => {
+  return canvas.referenceImages.entities.map(getEntityIdentifier).toReversed();
 });
 const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selectedEntityIdentifier) => {
   return selectedEntityIdentifier?.type === 'reference_image';
@@ -17,17 +17,17 @@ const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selecte
 
 export const IPAdapterList = memo(() => {
   const isSelected = useAppSelector(selectIsSelected);
-  const ipaIds = useAppSelector(selectEntityIds);
+  const entityIdentifiers = useAppSelector(selectEntityIdentifiers);
 
-  if (ipaIds.length === 0) {
+  if (entityIdentifiers.length === 0) {
     return null;
   }
 
-  if (ipaIds.length > 0) {
+  if (entityIdentifiers.length > 0) {
     return (
-      <CanvasEntityGroupList type="reference_image" isSelected={isSelected}>
-        {ipaIds.map((id) => (
-          <IPAdapter key={id} id={id} />
+      <CanvasEntityGroupList type="reference_image" isSelected={isSelected} entityIdentifiers={entityIdentifiers}>
+        {entityIdentifiers.map((entityIdentifiers) => (
+          <IPAdapter key={entityIdentifiers.id} id={entityIdentifiers.id} />
         ))}
       </CanvasEntityGroupList>
     );

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

@@ -19,11 +19,12 @@ import {
 import { selectIsFLUX } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice, selectEntityOrThrow } from 'features/controlLayers/store/selectors';
 import type { CLIPVisionModelV2, IPMethodV2 } from 'features/controlLayers/store/types';
-import type { IPAImageDropData } from 'features/dnd/types';
+import type { SetGlobalReferenceImageDndTargetData } from 'features/dnd/dnd';
+import { setGlobalReferenceImageDndTarget } from 'features/dnd/dnd';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiBoundingBoxBold } from 'react-icons/pi';
-import type { ImageDTO, IPAdapterModelConfig, IPALayerImagePostUploadAction } from 'services/api/types';
+import type { ImageDTO, IPAdapterModelConfig } from 'services/api/types';
 
 import { IPAdapterImagePreview } from './IPAdapterImagePreview';
 import { IPAdapterModel } from './IPAdapterModel';
@@ -80,13 +81,9 @@ export const IPAdapterSettings = memo(() => {
     [dispatch, entityIdentifier]
   );
 
-  const droppableData = useMemo<IPAImageDropData>(
-    () => ({ actionType: 'SET_IPA_IMAGE', context: { id: entityIdentifier.id }, id: entityIdentifier.id }),
-    [entityIdentifier.id]
-  );
-  const postUploadAction = useMemo<IPALayerImagePostUploadAction>(
-    () => ({ type: 'SET_IPA_IMAGE', id: entityIdentifier.id }),
-    [entityIdentifier.id]
+  const dndTargetData = useMemo<SetGlobalReferenceImageDndTargetData>(
+    () => setGlobalReferenceImageDndTarget.getData({ entityIdentifier }, ipAdapter.image?.image_name),
+    [entityIdentifier, ipAdapter.image?.image_name]
   );
   const pullBboxIntoIPAdapter = usePullBboxIntoGlobalReferenceImage(entityIdentifier);
   const isBusy = useCanvasIsBusy();
@@ -122,10 +119,10 @@ export const IPAdapterSettings = memo(() => {
           </Flex>
           <Flex alignItems="center" justifyContent="center" h={32} w={32} aspectRatio="1/1">
             <IPAdapterImagePreview
-              image={ipAdapter.image ?? null}
+              image={ipAdapter.image}
               onChangeImage={onChangeImage}
-              droppableData={droppableData}
-              postUploadAction={postUploadAction}
+              dndTarget={setGlobalReferenceImageDndTarget}
+              dndTargetData={dndTargetData}
             />
           </Flex>
         </Flex>

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

@@ -1,5 +1,5 @@
 import { Spacer } from '@invoke-ai/ui-library';
-import { CanvasEntityContainer } from 'features/controlLayers/components/common/CanvasEntityContainer';
+import { CanvasEntityContainer } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityContainer';
 import { CanvasEntityHeader } from 'features/controlLayers/components/common/CanvasEntityHeader';
 import { CanvasEntityHeaderCommonActions } from 'features/controlLayers/components/common/CanvasEntityHeaderCommonActions';
 import { CanvasEntityPreviewImage } from 'features/controlLayers/components/common/CanvasEntityPreviewImage';

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

@@ -1,14 +1,14 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { CanvasEntityGroupList } from 'features/controlLayers/components/common/CanvasEntityGroupList';
+import { CanvasEntityGroupList } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityGroupList';
 import { InpaintMask } from 'features/controlLayers/components/InpaintMask/InpaintMask';
-import { mapId } from 'features/controlLayers/konva/util';
 import { selectCanvasSlice, selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
+import { getEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo } from 'react';
 
-const selectEntityIds = createMemoizedSelector(selectCanvasSlice, (canvas) => {
-  return canvas.inpaintMasks.entities.map(mapId).reverse();
+const selectEntityIdentifiers = createMemoizedSelector(selectCanvasSlice, (canvas) => {
+  return canvas.inpaintMasks.entities.map(getEntityIdentifier).toReversed();
 });
 
 const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selectedEntityIdentifier) => {
@@ -17,17 +17,17 @@ const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selecte
 
 export const InpaintMaskList = memo(() => {
   const isSelected = useAppSelector(selectIsSelected);
-  const entityIds = useAppSelector(selectEntityIds);
+  const entityIdentifiers = useAppSelector(selectEntityIdentifiers);
 
-  if (entityIds.length === 0) {
+  if (entityIdentifiers.length === 0) {
     return null;
   }
 
-  if (entityIds.length > 0) {
+  if (entityIdentifiers.length > 0) {
     return (
-      <CanvasEntityGroupList type="inpaint_mask" isSelected={isSelected}>
-        {entityIds.map((id) => (
-          <InpaintMask key={id} id={id} />
+      <CanvasEntityGroupList type="inpaint_mask" isSelected={isSelected} entityIdentifiers={entityIdentifiers}>
+        {entityIdentifiers.map((entityIdentifier) => (
+          <InpaintMask key={entityIdentifier.id} id={entityIdentifier.id} />
         ))}
       </CanvasEntityGroupList>
     );

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

@@ -0,0 +1,82 @@
+import {
+  Badge,
+  CompositeNumberInput,
+  CompositeSlider,
+  Flex,
+  FormControl,
+  FormLabel,
+  useToken,
+} from '@invoke-ai/ui-library';
+import { createSelector } from '@reduxjs/toolkit';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
+import WavyLine from 'common/components/WavyLine';
+import { selectImg2imgStrength, setImg2imgStrength } from 'features/controlLayers/store/paramsSlice';
+import { selectActiveRasterLayerEntities } from 'features/controlLayers/store/selectors';
+import { selectImg2imgStrengthConfig } from 'features/system/store/configSlice';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+const selectIsEnabled = createSelector(selectActiveRasterLayerEntities, (entities) => entities.length > 0);
+
+export const ParamDenoisingStrength = memo(() => {
+  const img2imgStrength = useAppSelector(selectImg2imgStrength);
+  const dispatch = useAppDispatch();
+  const isEnabled = useAppSelector(selectIsEnabled);
+
+  const onChange = useCallback(
+    (v: number) => {
+      dispatch(setImg2imgStrength(v));
+    },
+    [dispatch]
+  );
+
+  const config = useAppSelector(selectImg2imgStrengthConfig);
+  const { t } = useTranslation();
+
+  const [invokeBlue300] = useToken('colors', ['invokeBlue.300']);
+
+  return (
+    <FormControl isDisabled={!isEnabled} p={1} justifyContent="space-between" h={8}>
+      <Flex gap={3} alignItems="center">
+        <InformationalPopover feature="paramDenoisingStrength">
+          <FormLabel mr={0}>{`${t('parameters.denoisingStrength')}`}</FormLabel>
+        </InformationalPopover>
+        {isEnabled && (
+          <WavyLine amplitude={img2imgStrength * 10} stroke={invokeBlue300} strokeWidth={1} width={40} height={14} />
+        )}
+      </Flex>
+      {isEnabled ? (
+        <>
+          <CompositeSlider
+            step={config.coarseStep}
+            fineStep={config.fineStep}
+            min={config.sliderMin}
+            max={config.sliderMax}
+            defaultValue={config.initial}
+            onChange={onChange}
+            value={img2imgStrength}
+          />
+          <CompositeNumberInput
+            step={config.coarseStep}
+            fineStep={config.fineStep}
+            min={config.numberInputMin}
+            max={config.numberInputMax}
+            defaultValue={config.initial}
+            onChange={onChange}
+            value={img2imgStrength}
+            variant="outline"
+          />
+        </>
+      ) : (
+        <Flex alignItems="center">
+          <Badge opacity="0.6">
+            {t('common.disabled')} - {t('parameters.noRasterLayers')}
+          </Badge>
+        </Flex>
+      )}
+    </FormControl>
+  );
+});
+
+ParamDenoisingStrength.displayName = 'ParamDenoisingStrength';

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

@@ -1,14 +1,16 @@
 import { Spacer } from '@invoke-ai/ui-library';
-import IAIDroppable from 'common/components/IAIDroppable';
-import { CanvasEntityContainer } from 'features/controlLayers/components/common/CanvasEntityContainer';
+import { CanvasEntityContainer } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityContainer';
 import { CanvasEntityHeader } from 'features/controlLayers/components/common/CanvasEntityHeader';
 import { CanvasEntityHeaderCommonActions } from 'features/controlLayers/components/common/CanvasEntityHeaderCommonActions';
 import { CanvasEntityPreviewImage } from 'features/controlLayers/components/common/CanvasEntityPreviewImage';
 import { CanvasEntityEditableTitle } from 'features/controlLayers/components/common/CanvasEntityTitleEdit';
 import { RasterLayerAdapterGate } from 'features/controlLayers/contexts/EntityAdapterContext';
 import { EntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
+import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
-import type { ReplaceLayerImageDropData } from 'features/dnd/types';
+import type { ReplaceCanvasEntityObjectsWithImageDndTargetData } from 'features/dnd/dnd';
+import { replaceCanvasEntityObjectsWithImageDndTarget } from 'features/dnd/dnd';
+import { DndDropTarget } from 'features/dnd/DndDropTarget';
 import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 
@@ -18,10 +20,11 @@ type Props = {
 
 export const RasterLayer = memo(({ id }: Props) => {
   const { t } = useTranslation();
+  const isBusy = useCanvasIsBusy();
   const entityIdentifier = useMemo<CanvasEntityIdentifier<'raster_layer'>>(() => ({ id, type: 'raster_layer' }), [id]);
-  const dropData = useMemo<ReplaceLayerImageDropData>(
-    () => ({ id, actionType: 'REPLACE_LAYER_WITH_IMAGE', context: { entityIdentifier } }),
-    [id, entityIdentifier]
+  const dndTargetData = useMemo<ReplaceCanvasEntityObjectsWithImageDndTargetData>(
+    () => replaceCanvasEntityObjectsWithImageDndTarget.getData({ entityIdentifier }, entityIdentifier.id),
+    [entityIdentifier]
   );
 
   return (
@@ -34,7 +37,12 @@ export const RasterLayer = memo(({ id }: Props) => {
             <Spacer />
             <CanvasEntityHeaderCommonActions />
           </CanvasEntityHeader>
-          <IAIDroppable data={dropData} dropLabel={t('controlLayers.replaceLayer')} />
+          <DndDropTarget
+            dndTarget={replaceCanvasEntityObjectsWithImageDndTarget}
+            dndTargetData={dndTargetData}
+            label={t('controlLayers.replaceLayer')}
+            isDisabled={isBusy}
+          />
         </CanvasEntityContainer>
       </RasterLayerAdapterGate>
     </EntityIdentifierContext.Provider>

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

@@ -1,14 +1,14 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { CanvasEntityGroupList } from 'features/controlLayers/components/common/CanvasEntityGroupList';
+import { CanvasEntityGroupList } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityGroupList';
 import { RasterLayer } from 'features/controlLayers/components/RasterLayer/RasterLayer';
-import { mapId } from 'features/controlLayers/konva/util';
 import { selectCanvasSlice, selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
+import { getEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo } from 'react';
 
-const selectEntityIds = createMemoizedSelector(selectCanvasSlice, (canvas) => {
-  return canvas.rasterLayers.entities.map(mapId).reverse();
+const selectEntityIdentifiers = createMemoizedSelector(selectCanvasSlice, (canvas) => {
+  return canvas.rasterLayers.entities.map(getEntityIdentifier).toReversed();
 });
 const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selectedEntityIdentifier) => {
   return selectedEntityIdentifier?.type === 'raster_layer';
@@ -16,17 +16,17 @@ const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selecte
 
 export const RasterLayerEntityList = memo(() => {
   const isSelected = useAppSelector(selectIsSelected);
-  const layerIds = useAppSelector(selectEntityIds);
+  const entityIdentifiers = useAppSelector(selectEntityIdentifiers);
 
-  if (layerIds.length === 0) {
+  if (entityIdentifiers.length === 0) {
     return null;
   }
 
-  if (layerIds.length > 0) {
+  if (entityIdentifiers.length > 0) {
     return (
-      <CanvasEntityGroupList type="raster_layer" isSelected={isSelected}>
-        {layerIds.map((id) => (
-          <RasterLayer key={id} id={id} />
+      <CanvasEntityGroupList type="raster_layer" isSelected={isSelected} entityIdentifiers={entityIdentifiers}>
+        {entityIdentifiers.map((entityIdentifier) => (
+          <RasterLayer key={entityIdentifier.id} id={entityIdentifier.id} />
         ))}
       </CanvasEntityGroupList>
     );

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

@@ -1,8 +1,8 @@
 import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAppDispatch } from 'app/store/storeHooks';
 import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
+import { deepClone } from 'common/util/deepClone';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { selectDefaultControlAdapter } from 'features/controlLayers/hooks/addLayerHooks';
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import { useEntityIsLocked } from 'features/controlLayers/hooks/useEntityIsLocked';
 import {
@@ -10,6 +10,7 @@ import {
   rasterLayerConvertedToInpaintMask,
   rasterLayerConvertedToRegionalGuidance,
 } from 'features/controlLayers/store/canvasSlice';
+import { initialControlNet } from 'features/controlLayers/store/util';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiSwapBold } from 'react-icons/pi';
@@ -20,7 +21,6 @@ export const RasterLayerMenuItemsConvertToSubMenu = memo(() => {
 
   const dispatch = useAppDispatch();
   const entityIdentifier = useEntityIdentifierContext('raster_layer');
-  const defaultControlAdapter = useAppSelector(selectDefaultControlAdapter);
   const isBusy = useCanvasIsBusy();
   const isLocked = useEntityIsLocked(entityIdentifier);
 
@@ -37,10 +37,10 @@ export const RasterLayerMenuItemsConvertToSubMenu = memo(() => {
       rasterLayerConvertedToControlLayer({
         entityIdentifier,
         replace: true,
-        overrides: { controlAdapter: defaultControlAdapter },
+        overrides: { controlAdapter: deepClone(initialControlNet) },
       })
     );
-  }, [defaultControlAdapter, dispatch, entityIdentifier]);
+  }, [dispatch, entityIdentifier]);
 
   return (
     <MenuItem {...subMenu.parentMenuItemProps} icon={<PiSwapBold />} isDisabled={isBusy || isLocked}>

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

@@ -1,15 +1,16 @@
 import { Menu, MenuButton, MenuItem, MenuList } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAppDispatch } from 'app/store/storeHooks';
 import { SubMenuButtonContent, useSubMenu } from 'common/hooks/useSubMenu';
+import { deepClone } from 'common/util/deepClone';
 import { CanvasEntityMenuItemsCopyToClipboard } from 'features/controlLayers/components/common/CanvasEntityMenuItemsCopyToClipboard';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { selectDefaultControlAdapter } from 'features/controlLayers/hooks/addLayerHooks';
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import {
   rasterLayerConvertedToControlLayer,
   rasterLayerConvertedToInpaintMask,
   rasterLayerConvertedToRegionalGuidance,
 } from 'features/controlLayers/store/canvasSlice';
+import { initialControlNet } from 'features/controlLayers/store/util';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiCopyBold } from 'react-icons/pi';
@@ -20,7 +21,6 @@ export const RasterLayerMenuItemsCopyToSubMenu = memo(() => {
 
   const dispatch = useAppDispatch();
   const entityIdentifier = useEntityIdentifierContext('raster_layer');
-  const defaultControlAdapter = useAppSelector(selectDefaultControlAdapter);
   const isBusy = useCanvasIsBusy();
 
   const copyToInpaintMask = useCallback(() => {
@@ -35,10 +35,10 @@ export const RasterLayerMenuItemsCopyToSubMenu = memo(() => {
     dispatch(
       rasterLayerConvertedToControlLayer({
         entityIdentifier,
-        overrides: { controlAdapter: defaultControlAdapter },
+        overrides: { controlAdapter: deepClone(initialControlNet) },
       })
     );
-  }, [defaultControlAdapter, dispatch, entityIdentifier]);
+  }, [dispatch, entityIdentifier]);
 
   return (
     <MenuItem {...subMenu.parentMenuItemProps} icon={<PiCopyBold />} isDisabled={isBusy}>

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

@@ -1,5 +1,5 @@
 import { Spacer } from '@invoke-ai/ui-library';
-import { CanvasEntityContainer } from 'features/controlLayers/components/common/CanvasEntityContainer';
+import { CanvasEntityContainer } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityContainer';
 import { CanvasEntityHeader } from 'features/controlLayers/components/common/CanvasEntityHeader';
 import { CanvasEntityHeaderCommonActions } from 'features/controlLayers/components/common/CanvasEntityHeaderCommonActions';
 import { CanvasEntityPreviewImage } from 'features/controlLayers/components/common/CanvasEntityPreviewImage';

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

@@ -1,14 +1,14 @@
 import { createSelector } from '@reduxjs/toolkit';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppSelector } from 'app/store/storeHooks';
-import { CanvasEntityGroupList } from 'features/controlLayers/components/common/CanvasEntityGroupList';
+import { CanvasEntityGroupList } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityGroupList';
 import { RegionalGuidance } from 'features/controlLayers/components/RegionalGuidance/RegionalGuidance';
-import { mapId } from 'features/controlLayers/konva/util';
 import { selectCanvasSlice, selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
+import { getEntityIdentifier } from 'features/controlLayers/store/types';
 import { memo } from 'react';
 
-const selectEntityIds = createMemoizedSelector(selectCanvasSlice, (canvas) => {
-  return canvas.regionalGuidance.entities.map(mapId).reverse();
+const selectEntityIdentifiers = createMemoizedSelector(selectCanvasSlice, (canvas) => {
+  return canvas.regionalGuidance.entities.map(getEntityIdentifier).toReversed();
 });
 const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selectedEntityIdentifier) => {
   return selectedEntityIdentifier?.type === 'regional_guidance';
@@ -16,17 +16,17 @@ const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selecte
 
 export const RegionalGuidanceEntityList = memo(() => {
   const isSelected = useAppSelector(selectIsSelected);
-  const rgIds = useAppSelector(selectEntityIds);
+  const entityIdentifiers = useAppSelector(selectEntityIdentifiers);
 
-  if (rgIds.length === 0) {
+  if (entityIdentifiers.length === 0) {
     return null;
   }
 
-  if (rgIds.length > 0) {
+  if (entityIdentifiers.length > 0) {
     return (
-      <CanvasEntityGroupList type="regional_guidance" isSelected={isSelected}>
-        {rgIds.map((id) => (
-          <RegionalGuidance key={id} id={id} />
+      <CanvasEntityGroupList type="regional_guidance" isSelected={isSelected} entityIdentifiers={entityIdentifiers}>
+        {entityIdentifiers.map((entityIdentifier) => (
+          <RegionalGuidance key={entityIdentifier.id} id={entityIdentifier.id} />
         ))}
       </CanvasEntityGroupList>
     );

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

@@ -20,11 +20,12 @@ import {
 } from 'features/controlLayers/store/canvasSlice';
 import { selectCanvasSlice, selectRegionalGuidanceReferenceImage } from 'features/controlLayers/store/selectors';
 import type { CLIPVisionModelV2, IPMethodV2 } from 'features/controlLayers/store/types';
-import type { RGIPAdapterImageDropData } from 'features/dnd/types';
+import type { SetRegionalGuidanceReferenceImageDndTargetData } from 'features/dnd/dnd';
+import { setRegionalGuidanceReferenceImageDndTarget } from 'features/dnd/dnd';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiBoundingBoxBold, PiTrashSimpleFill } from 'react-icons/pi';
-import type { ImageDTO, IPAdapterModelConfig, RGIPAdapterImagePostUploadAction } from 'services/api/types';
+import type { ImageDTO, IPAdapterModelConfig } from 'services/api/types';
 import { assert } from 'tsafe';
 
 type Props = {
@@ -91,18 +92,15 @@ export const RegionalGuidanceIPAdapterSettings = memo(({ referenceImageId }: Pro
     [dispatch, entityIdentifier, referenceImageId]
   );
 
-  const droppableData = useMemo<RGIPAdapterImageDropData>(
-    () => ({
-      actionType: 'SET_RG_IP_ADAPTER_IMAGE',
-      context: { id: entityIdentifier.id, referenceImageId: referenceImageId },
-      id: entityIdentifier.id,
-    }),
-    [entityIdentifier.id, referenceImageId]
-  );
-  const postUploadAction = useMemo<RGIPAdapterImagePostUploadAction>(
-    () => ({ type: 'SET_RG_IP_ADAPTER_IMAGE', id: entityIdentifier.id, referenceImageId: referenceImageId }),
-    [entityIdentifier.id, referenceImageId]
+  const dndTargetData = useMemo<SetRegionalGuidanceReferenceImageDndTargetData>(
+    () =>
+      setRegionalGuidanceReferenceImageDndTarget.getData(
+        { entityIdentifier, referenceImageId },
+        ipAdapter.image?.image_name
+      ),
+    [entityIdentifier, ipAdapter.image?.image_name, referenceImageId]
   );
+
   const pullBboxIntoIPAdapter = usePullBboxIntoRegionalGuidanceReferenceImage(entityIdentifier, referenceImageId);
   const isBusy = useCanvasIsBusy();
 
@@ -151,10 +149,10 @@ export const RegionalGuidanceIPAdapterSettings = memo(({ referenceImageId }: Pro
           </Flex>
           <Flex alignItems="center" justifyContent="center" h={32} w={32} aspectRatio="1/1">
             <IPAdapterImagePreview
-              image={ipAdapter.image ?? null}
+              image={ipAdapter.image}
               onChangeImage={onChangeImage}
-              droppableData={droppableData}
-              postUploadAction={postUploadAction}
+              dndTarget={setRegionalGuidanceReferenceImageDndTarget}
+              dndTargetData={dndTargetData}
             />
           </Flex>
         </Flex>

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

@@ -1,38 +0,0 @@
-import { Flex } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
-import { useEntityIsSelected } from 'features/controlLayers/hooks/useEntityIsSelected';
-import { useEntitySelectionColor } from 'features/controlLayers/hooks/useEntitySelectionColor';
-import { entitySelected } from 'features/controlLayers/store/canvasSlice';
-import type { PropsWithChildren } from 'react';
-import { memo, useCallback } from 'react';
-
-export const CanvasEntityContainer = memo((props: PropsWithChildren) => {
-  const dispatch = useAppDispatch();
-  const entityIdentifier = useEntityIdentifierContext();
-  const isSelected = useEntityIsSelected(entityIdentifier);
-  const selectionColor = useEntitySelectionColor(entityIdentifier);
-  const onClick = useCallback(() => {
-    if (isSelected) {
-      return;
-    }
-    dispatch(entitySelected({ entityIdentifier }));
-  }, [dispatch, entityIdentifier, isSelected]);
-
-  return (
-    <Flex
-      position="relative"
-      flexDir="column"
-      w="full"
-      bg={isSelected ? 'base.800' : 'base.850'}
-      onClick={onClick}
-      borderInlineStartWidth={5}
-      borderColor={isSelected ? selectionColor : 'base.800'}
-      borderRadius="base"
-    >
-      {props.children}
-    </Flex>
-  );
-});
-
-CanvasEntityContainer.displayName = 'CanvasEntityContainer';

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

@@ -1,90 +0,0 @@
-import type { SystemStyleObject } from '@invoke-ai/ui-library';
-import { Button, Collapse, Flex, Icon, Spacer, Text } from '@invoke-ai/ui-library';
-import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
-import { useBoolean } from 'common/hooks/useBoolean';
-import { CanvasEntityAddOfTypeButton } from 'features/controlLayers/components/common/CanvasEntityAddOfTypeButton';
-import { CanvasEntityMergeVisibleButton } from 'features/controlLayers/components/common/CanvasEntityMergeVisibleButton';
-import { CanvasEntityTypeIsHiddenToggle } from 'features/controlLayers/components/common/CanvasEntityTypeIsHiddenToggle';
-import { useEntityTypeInformationalPopover } from 'features/controlLayers/hooks/useEntityTypeInformationalPopover';
-import { useEntityTypeTitle } from 'features/controlLayers/hooks/useEntityTypeTitle';
-import { type CanvasEntityIdentifier, isRenderableEntityType } from 'features/controlLayers/store/types';
-import type { PropsWithChildren } from 'react';
-import { memo } from 'react';
-import { PiCaretDownBold } from 'react-icons/pi';
-
-type Props = PropsWithChildren<{
-  isSelected: boolean;
-  type: CanvasEntityIdentifier['type'];
-}>;
-
-const _hover: SystemStyleObject = {
-  opacity: 1,
-};
-
-export const CanvasEntityGroupList = memo(({ isSelected, type, children }: Props) => {
-  const title = useEntityTypeTitle(type);
-  const informationalPopoverFeature = useEntityTypeInformationalPopover(type);
-  const collapse = useBoolean(true);
-
-  return (
-    <Flex flexDir="column" w="full">
-      <Flex w="full">
-        <Flex
-          flexGrow={1}
-          as={Button}
-          onClick={collapse.toggle}
-          justifyContent="space-between"
-          alignItems="center"
-          gap={3}
-          variant="unstyled"
-          p={0}
-          h={8}
-        >
-          <Icon
-            boxSize={4}
-            as={PiCaretDownBold}
-            transform={collapse.isTrue ? undefined : 'rotate(-90deg)'}
-            fill={isSelected ? 'base.200' : 'base.500'}
-            transitionProperty="common"
-            transitionDuration="fast"
-          />
-          {informationalPopoverFeature ? (
-            <InformationalPopover feature={informationalPopoverFeature}>
-              <Text
-                fontWeight="semibold"
-                color={isSelected ? 'base.200' : 'base.500'}
-                userSelect="none"
-                transitionProperty="common"
-                transitionDuration="fast"
-              >
-                {title}
-              </Text>
-            </InformationalPopover>
-          ) : (
-            <Text
-              fontWeight="semibold"
-              color={isSelected ? 'base.200' : 'base.500'}
-              userSelect="none"
-              transitionProperty="common"
-              transitionDuration="fast"
-            >
-              {title}
-            </Text>
-          )}
-
-          <Spacer />
-        </Flex>
-        {isRenderableEntityType(type) && <CanvasEntityMergeVisibleButton type={type} />}
-        {isRenderableEntityType(type) && <CanvasEntityTypeIsHiddenToggle type={type} />}
-        <CanvasEntityAddOfTypeButton type={type} />
-      </Flex>
-      <Collapse in={collapse.isTrue}>
-        <Flex flexDir="column" gap={2} pt={2}>
-          {children}
-        </Flex>
-      </Collapse>
-    </Flex>
-  );
-});
-
-CanvasEntityGroupList.displayName = 'CanvasEntityGroupList';

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

@@ -1,4 +1,4 @@
-import { Box, chakra, Flex } from '@invoke-ai/ui-library';
+import { Box, chakra, Flex, Tooltip } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { createSelector } from '@reduxjs/toolkit';
 import { rgbColorToString } from 'common/util/colorCodeTransformers';
@@ -86,13 +86,63 @@ export const CanvasEntityPreviewImage = memo(() => {
 
   useEffect(updatePreview, [updatePreview, canvasCache, nodeRect, pixelRect]);
 
+  return (
+    <Tooltip label={<TooltipContent canvasRef={canvasRef} />} p={2} closeOnScroll>
+      <Flex
+        position="relative"
+        alignItems="center"
+        justifyContent="center"
+        w={CONTAINER_WIDTH_PX}
+        h={CONTAINER_WIDTH_PX}
+        borderRadius="sm"
+        borderWidth={1}
+        bg="base.900"
+        flexShrink={0}
+      >
+        <Box
+          position="absolute"
+          top={0}
+          right={0}
+          bottom={0}
+          left={0}
+          bgImage={TRANSPARENCY_CHECKERBOARD_PATTERN_DARK_DATAURL}
+          bgSize="5px"
+        />
+        <ChakraCanvas position="relative" ref={canvasRef} objectFit="contain" maxW="full" maxH="full" />
+      </Flex>
+    </Tooltip>
+  );
+});
+
+CanvasEntityPreviewImage.displayName = 'CanvasEntityPreviewImage';
+
+const TooltipContent = ({ canvasRef }: { canvasRef: React.RefObject<HTMLCanvasElement> }) => {
+  const canvasRef2 = useRef<HTMLCanvasElement>(null);
+
+  useEffect(() => {
+    if (!canvasRef2.current || !canvasRef.current) {
+      return;
+    }
+
+    const ctx = canvasRef2.current.getContext('2d');
+
+    if (!ctx) {
+      return;
+    }
+
+    canvasRef2.current.width = canvasRef.current.width;
+    canvasRef2.current.height = canvasRef.current.height;
+    ctx.clearRect(0, 0, canvasRef2.current.width, canvasRef2.current.height);
+    ctx.drawImage(canvasRef.current, 0, 0);
+  }, [canvasRef]);
+
   return (
     <Flex
       position="relative"
       alignItems="center"
       justifyContent="center"
-      w={CONTAINER_WIDTH_PX}
-      h={CONTAINER_WIDTH_PX}
+      w={150}
+      h={150}
       borderRadius="sm"
       borderWidth={1}
       bg="base.900"
@@ -105,11 +155,9 @@ export const CanvasEntityPreviewImage = memo(() => {
         bottom={0}
         left={0}
         bgImage={TRANSPARENCY_CHECKERBOARD_PATTERN_DARK_DATAURL}
-        bgSize="5px"
+        bgSize="8px"
       />
-      <ChakraCanvas position="relative" ref={canvasRef} objectFit="contain" maxW="full" maxH="full" />
+      <ChakraCanvas position="relative" ref={canvasRef2} objectFit="contain" maxW="full" maxH="full" />
     </Flex>
   );
-});
-
-CanvasEntityPreviewImage.displayName = 'CanvasEntityPreviewImage';
+};

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

@@ -4,9 +4,10 @@ import type { CanvasEntityAdapterControlLayer } from 'features/controlLayers/kon
 import type { CanvasEntityAdapterInpaintMask } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterInpaintMask';
 import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterRasterLayer';
 import type { CanvasEntityAdapterRegionalGuidance } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterRegionalGuidance';
-import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
+import type { CanvasEntityAdapterFromType } from 'features/controlLayers/konva/CanvasEntity/types';
+import type { CanvasEntityIdentifier, CanvasRenderableEntityType } from 'features/controlLayers/store/types';
 import type { PropsWithChildren } from 'react';
-import { createContext, memo, useMemo, useSyncExternalStore } from 'react';
+import { createContext, memo, useContext, useMemo, useSyncExternalStore } from 'react';
 import { assert } from 'tsafe';
 
 const EntityAdapterContext = createContext<
@@ -95,6 +96,17 @@ export const RegionalGuidanceAdapterGate = memo(({ children }: PropsWithChildren
   return <EntityAdapterContext.Provider value={adapter}>{children}</EntityAdapterContext.Provider>;
 });
 
+export const useEntityAdapterContext = <T extends CanvasRenderableEntityType | undefined = CanvasRenderableEntityType>(
+  type?: T
+): CanvasEntityAdapterFromType<T extends undefined ? CanvasRenderableEntityType : T> => {
+  const adapter = useContext(EntityAdapterContext);
+  assert(adapter, 'useEntityIdentifier must be used within a EntityIdentifierProvider');
+  if (type) {
+    assert(adapter.entityIdentifier.type === type, 'useEntityIdentifier must be used with the correct type');
+  }
+  return adapter as CanvasEntityAdapterFromType<T extends undefined ? CanvasRenderableEntityType : T>;
+};
+
 RegionalGuidanceAdapterGate.displayName = 'RegionalGuidanceAdapterGate';
 
 export const useEntityAdapterSafe = (

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

@@ -2,11 +2,8 @@ import { createSelector } from '@reduxjs/toolkit';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { deepClone } from 'common/util/deepClone';
-import { CanvasEntityAdapterBase } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterBase';
 import { getPrefixedId } from 'features/controlLayers/konva/util';
-import { canvasReset } from 'features/controlLayers/store/actions';
 import {
-  bboxChangedFromCanvas,
   controlLayerAdded,
   inpaintMaskAdded,
   rasterLayerAdded,
@@ -17,38 +14,22 @@ import {
   rgPositivePromptChanged,
 } from 'features/controlLayers/store/canvasSlice';
 import { selectBase } from 'features/controlLayers/store/paramsSlice';
-import {
-  selectBboxModelBase,
-  selectBboxRect,
-  selectCanvasSlice,
-  selectEntityOrThrow,
-} from 'features/controlLayers/store/selectors';
+import { selectCanvasSlice, selectEntityOrThrow } from 'features/controlLayers/store/selectors';
 import type {
-  CanvasControlLayerState,
   CanvasEntityIdentifier,
-  CanvasInpaintMaskState,
-  CanvasRasterLayerState,
   CanvasRegionalGuidanceState,
   ControlNetConfig,
   IPAdapterConfig,
   T2IAdapterConfig,
 } from 'features/controlLayers/store/types';
-import {
-  imageDTOToImageObject,
-  initialControlNet,
-  initialIPAdapter,
-  initialT2IAdapter,
-} from 'features/controlLayers/store/util';
-import { calculateNewSize } from 'features/controlLayers/util/getScaledBoundingBoxDimensions';
+import { initialControlNet, initialIPAdapter, initialT2IAdapter } from 'features/controlLayers/store/util';
 import { zModelIdentifierField } from 'features/nodes/types/common';
-import { getOptimalDimension } from 'features/parameters/util/optimalDimension';
 import { useCallback } from 'react';
 import { modelConfigsAdapterSelectors, selectModelConfigsQuery } from 'services/api/endpoints/models';
-import type { ControlNetModelConfig, ImageDTO, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
+import type { ControlNetModelConfig, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
 import { isControlNetOrT2IAdapterModelConfig, isIPAdapterModelConfig } from 'services/api/types';
-import type { Equals } from 'tsafe';
-import { assert } from 'tsafe';
 
+/** @knipignore */
 export const selectDefaultControlAdapter = createSelector(
   selectModelConfigsQuery,
   selectBase,
@@ -85,6 +66,9 @@ export const selectDefaultIPAdapter = createSelector(
     const ipAdapter = deepClone(initialIPAdapter);
     if (model) {
       ipAdapter.model = zModelIdentifierField.parse(model);
+      if (model.base === 'flux') {
+        ipAdapter.clipVisionModel = 'ViT-L';
+      }
     }
     return ipAdapter;
   }
@@ -92,11 +76,10 @@ export const selectDefaultIPAdapter = createSelector(
 
 export const useAddControlLayer = () => {
   const dispatch = useAppDispatch();
-  const defaultControlAdapter = useAppSelector(selectDefaultControlAdapter);
   const func = useCallback(() => {
-    const overrides = { controlAdapter: defaultControlAdapter };
+    const overrides = { controlAdapter: deepClone(initialControlNet) };
     dispatch(controlLayerAdded({ isSelected: true, overrides }));
-  }, [defaultControlAdapter, dispatch]);
+  }, [dispatch]);
 
   return func;
 };
@@ -110,150 +93,6 @@ export const useAddRasterLayer = () => {
   return func;
 };
 
-export const useNewRasterLayerFromImage = () => {
-  const dispatch = useAppDispatch();
-  const bboxRect = useAppSelector(selectBboxRect);
-  const func = useCallback(
-    (imageDTO: ImageDTO) => {
-      const imageObject = imageDTOToImageObject(imageDTO);
-      const overrides: Partial<CanvasRasterLayerState> = {
-        position: { x: bboxRect.x, y: bboxRect.y },
-        objects: [imageObject],
-      };
-      dispatch(rasterLayerAdded({ overrides, isSelected: true }));
-    },
-    [bboxRect.x, bboxRect.y, dispatch]
-  );
-
-  return func;
-};
-
-export const useNewControlLayerFromImage = () => {
-  const dispatch = useAppDispatch();
-  const bboxRect = useAppSelector(selectBboxRect);
-  const func = useCallback(
-    (imageDTO: ImageDTO) => {
-      const imageObject = imageDTOToImageObject(imageDTO);
-      const overrides: Partial<CanvasControlLayerState> = {
-        position: { x: bboxRect.x, y: bboxRect.y },
-        objects: [imageObject],
-      };
-      dispatch(controlLayerAdded({ overrides, isSelected: true }));
-    },
-    [bboxRect.x, bboxRect.y, dispatch]
-  );
-
-  return func;
-};
-
-export const useNewInpaintMaskFromImage = () => {
-  const dispatch = useAppDispatch();
-  const bboxRect = useAppSelector(selectBboxRect);
-  const func = useCallback(
-    (imageDTO: ImageDTO) => {
-      const imageObject = imageDTOToImageObject(imageDTO);
-      const overrides: Partial<CanvasInpaintMaskState> = {
-        position: { x: bboxRect.x, y: bboxRect.y },
-        objects: [imageObject],
-      };
-      dispatch(inpaintMaskAdded({ overrides, isSelected: true }));
-    },
-    [bboxRect.x, bboxRect.y, dispatch]
-  );
-
-  return func;
-};
-
-export const useNewRegionalGuidanceFromImage = () => {
-  const dispatch = useAppDispatch();
-  const bboxRect = useAppSelector(selectBboxRect);
-  const func = useCallback(
-    (imageDTO: ImageDTO) => {
-      const imageObject = imageDTOToImageObject(imageDTO);
-      const overrides: Partial<CanvasRegionalGuidanceState> = {
-        position: { x: bboxRect.x, y: bboxRect.y },
-        objects: [imageObject],
-      };
-      dispatch(rgAdded({ overrides, isSelected: true }));
-    },
-    [bboxRect.x, bboxRect.y, dispatch]
-  );
-
-  return func;
-};
-
-/**
- * Returns a function that adds a new canvas with the given image as the initial image, replicating the img2img flow:
- * - Reset the canvas
- * - Resize the bbox to the image's aspect ratio at the optimal size for the selected model
- * - Add the image as a raster layer
- * - Resizes the layer to fit the bbox using the 'fill' strategy
- *
- * This allows the user to immediately generate a new image from the given image without any additional steps.
- */
-export const useNewCanvasFromImage = () => {
-  const dispatch = useAppDispatch();
-  const bboxRect = useAppSelector(selectBboxRect);
-  const base = useAppSelector(selectBboxModelBase);
-  const func = useCallback(
-    (imageDTO: ImageDTO, type: CanvasRasterLayerState['type'] | CanvasControlLayerState['type']) => {
-      // Calculate the new bbox dimensions to fit the image's aspect ratio at the optimal size
-      const ratio = imageDTO.width / imageDTO.height;
-      const optimalDimension = getOptimalDimension(base);
-      const { width, height } = calculateNewSize(ratio, optimalDimension ** 2, base);
-
-      // The overrides need to include the layer's ID so we can transform the layer it is initialized
-      let overrides: Partial<CanvasRasterLayerState> | Partial<CanvasControlLayerState>;
-
-      if (type === 'raster_layer') {
-        overrides = {
-          id: getPrefixedId('raster_layer'),
-          position: { x: bboxRect.x, y: bboxRect.y },
-          objects: [imageDTOToImageObject(imageDTO)],
-        } satisfies Partial<CanvasRasterLayerState>;
-      } else if (type === 'control_layer') {
-        overrides = {
-          id: getPrefixedId('control_layer'),
-          position: { x: bboxRect.x, y: bboxRect.y },
-          objects: [imageDTOToImageObject(imageDTO)],
-        } satisfies Partial<CanvasControlLayerState>;
-      } else {
-        // Catch unhandled types
-        assert<Equals<typeof type, never>>(false);
-      }
-
-      CanvasEntityAdapterBase.registerInitCallback(async (adapter) => {
-        // Skip the callback if the adapter is not the one we are creating
-        if (adapter.id !== overrides.id) {
-          return false;
-        }
-        // Fit the layer to the bbox w/ fill strategy
-        await adapter.transformer.startTransform({ silent: true });
-        adapter.transformer.fitToBboxFill();
-        await adapter.transformer.applyTransform();
-        return true;
-      });
-
-      dispatch(canvasReset());
-      // The `bboxChangedFromCanvas` reducer does no validation! Careful!
-      dispatch(bboxChangedFromCanvas({ x: 0, y: 0, width, height }));
-
-      // The type casts are safe because the type is checked above
-      if (type === 'raster_layer') {
-        dispatch(rasterLayerAdded({ overrides: overrides as Partial<CanvasRasterLayerState>, isSelected: true }));
-      } else if (type === 'control_layer') {
-        dispatch(controlLayerAdded({ overrides: overrides as Partial<CanvasControlLayerState>, isSelected: true }));
-      } else {
-        // Catch unhandled types
-        assert<Equals<typeof type, never>>(false);
-      }
-    },
-    [base, bboxRect.x, bboxRect.y, dispatch]
-  );
-
-  return func;
-};
-
 export const useAddInpaintMask = () => {
   const dispatch = useAppDispatch();
   const func = useCallback(() => {

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

@@ -1,10 +1,9 @@
 import { logger } from 'app/logging/logger';
 import { useAppDispatch, useAppSelector, useAppStore } from 'app/store/storeHooks';
-import type { SerializableObject } from 'common/types';
 import { deepClone } from 'common/util/deepClone';
 import { withResultAsync } from 'common/util/result';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { selectDefaultControlAdapter, selectDefaultIPAdapter } from 'features/controlLayers/hooks/addLayerHooks';
+import { selectDefaultIPAdapter } from 'features/controlLayers/hooks/addLayerHooks';
 import { getPrefixedId } from 'features/controlLayers/konva/util';
 import {
   controlLayerAdded,
@@ -25,12 +24,13 @@ import type {
   Rect,
   RegionalGuidanceReferenceImageState,
 } from 'features/controlLayers/store/types';
-import { imageDTOToImageObject, imageDTOToImageWithDims } from 'features/controlLayers/store/util';
+import { imageDTOToImageObject, imageDTOToImageWithDims, initialControlNet } from 'features/controlLayers/store/util';
 import { toast } from 'features/toast/toast';
 import { useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { serializeError } from 'serialize-error';
 import type { ImageDTO } from 'services/api/types';
+import type { JsonObject } from 'type-fest';
 
 const log = logger('canvas');
 
@@ -64,7 +64,7 @@ const useSaveCanvas = ({ region, saveToGallery, toastOk, toastError, onSave, wit
       return;
     }
 
-    let metadata: SerializableObject | undefined = undefined;
+    let metadata: JsonObject | undefined = undefined;
 
     if (withMetadata) {
       metadata = selectCanvasMetadata(store.getState());
@@ -72,10 +72,16 @@ const useSaveCanvas = ({ region, saveToGallery, toastOk, toastError, onSave, wit
 
     const result = await withResultAsync(() => {
       const rasterAdapters = canvasManager.compositor.getVisibleAdaptersOfType('raster_layer');
-      return canvasManager.compositor.getCompositeImageDTO(rasterAdapters, rect, {
-        is_intermediate: !saveToGallery,
-        metadata,
-      });
+      return canvasManager.compositor.getCompositeImageDTO(
+        rasterAdapters,
+        rect,
+        {
+          is_intermediate: !saveToGallery,
+          metadata,
+        },
+        undefined,
+        true // force upload the image to ensure it gets added to the gallery
+      );
     });
 
     if (result.isOk()) {
@@ -223,13 +229,12 @@ export const useNewRasterLayerFromBbox = () => {
 export const useNewControlLayerFromBbox = () => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
-  const defaultControlAdapter = useAppSelector(selectDefaultControlAdapter);
 
   const arg = useMemo<UseSaveCanvasArg>(() => {
     const onSave = (imageDTO: ImageDTO, rect: Rect) => {
       const overrides: Partial<CanvasControlLayerState> = {
         objects: [imageDTOToImageObject(imageDTO)],
-        controlAdapter: deepClone(defaultControlAdapter),
+        controlAdapter: deepClone(initialControlNet),
         position: { x: rect.x, y: rect.y },
       };
       dispatch(controlLayerAdded({ overrides, isSelected: true }));
@@ -242,7 +247,7 @@ export const useNewControlLayerFromBbox = () => {
       toastOk: t('controlLayers.newControlLayerOk'),
       toastError: t('controlLayers.newControlLayerError'),
     };
-  }, [defaultControlAdapter, dispatch, t]);
+  }, [dispatch, t]);
   const func = useSaveCanvas(arg);
   return func;
 };

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

@@ -1,27 +0,0 @@
-import { createSelector } from '@reduxjs/toolkit';
-import { useAppSelector } from 'app/store/storeHooks';
-import { rgbColorToString } from 'common/util/colorCodeTransformers';
-import { selectCanvasSlice, selectEntity } from 'features/controlLayers/store/selectors';
-import type { CanvasEntityIdentifier } from 'features/controlLayers/store/types';
-import { useMemo } from 'react';
-
-export const useEntitySelectionColor = (entityIdentifier: CanvasEntityIdentifier) => {
-  const selectSelectionColor = useMemo(
-    () =>
-      createSelector(selectCanvasSlice, (canvas) => {
-        const entity = selectEntity(canvas, entityIdentifier);
-        if (!entity) {
-          return 'base.400';
-        } else if (entity.type === 'inpaint_mask') {
-          return rgbColorToString(entity.fill.color);
-        } else if (entity.type === 'regional_guidance') {
-          return rgbColorToString(entity.fill.color);
-        } else {
-          return 'base.400';
-        }
-      }),
-    [entityIdentifier]
-  );
-  const selectionColor = useAppSelector(selectSelectionColor);
-  return selectionColor;
-};

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

@@ -5,14 +5,10 @@ import type { CanvasEntityAdapterRasterLayer } from 'features/controlLayers/konv
 import type { CanvasEntityAdapterRegionalGuidance } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterRegionalGuidance';
 import { canvasToBlob } from 'features/controlLayers/konva/util';
 import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
-import { toast } from 'features/toast/toast';
 import { useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { useUploadImageMutation } from 'services/api/endpoints/images';
+import { uploadImage } from 'services/api/endpoints/images';
 
 export const useSaveLayerToAssets = () => {
-  const { t } = useTranslation();
-  const [uploadImage] = useUploadImageMutation();
   const autoAddBoardId = useAppSelector(selectAutoAddBoardId);
 
   const saveLayerToAssets = useCallback(
@@ -27,30 +23,17 @@ export const useSaveLayerToAssets = () => {
       if (!adapter) {
         return;
       }
-      try {
-        const canvas = adapter.getCanvas();
-        const blob = await canvasToBlob(canvas);
-        const file = new File([blob], `layer-${adapter.id}.png`, { type: 'image/png' });
-        await uploadImage({
-          file,
-          image_category: 'user',
-          is_intermediate: false,
-          postUploadAction: { type: 'TOAST' },
-          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-        });
-
-        toast({
-          status: 'info',
-          title: t('toast.layerSavedToAssets'),
-        });
-      } catch (error) {
-        toast({
-          status: 'error',
-          title: t('toast.problemSavingLayer'),
-        });
-      }
+      const canvas = adapter.getCanvas();
+      const blob = await canvasToBlob(canvas);
+      const file = new File([blob], `layer-${adapter.id}.png`, { type: 'image/png' });
+      uploadImage({
+        file,
+        image_category: 'user',
+        is_intermediate: false,
+        board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+      });
     },
-    [t, autoAddBoardId, uploadImage]
+    [autoAddBoardId]
   );
 
   return saveLayerToAssets;

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

@@ -1,4 +1,3 @@
-import type { SerializableObject } from 'common/types';
 import { withResultAsync } from 'common/util/result';
 import { CanvasCacheModule } from 'features/controlLayers/konva/CanvasCacheModule';
 import type { CanvasEntityAdapter, CanvasEntityAdapterFromType } from 'features/controlLayers/konva/CanvasEntity/types';
@@ -35,12 +34,13 @@ import { t } from 'i18next';
 import { atom, computed } from 'nanostores';
 import type { Logger } from 'roarr';
 import { serializeError } from 'serialize-error';
-import type { UploadOptions } from 'services/api/endpoints/images';
+import type { UploadImageArg } from 'services/api/endpoints/images';
 import { getImageDTOSafe, uploadImage } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 import stableHash from 'stable-hash';
 import type { Equals } from 'tsafe';
 import { assert } from 'tsafe';
+import type { JsonObject } from 'type-fest';
 
 type CompositingOptions = {
   /**
@@ -173,14 +173,14 @@ export class CanvasCompositorModule extends CanvasModuleBase {
     return adapters as CanvasEntityAdapterFromType<T>[];
   };
 
-  getCompositeHash = (adapters: CanvasEntityAdapter[], extra: SerializableObject): string => {
-    const adapterHashes: SerializableObject[] = [];
+  getCompositeHash = (adapters: CanvasEntityAdapter[], extra: JsonObject): string => {
+    const adapterHashes: JsonObject[] = [];
 
     for (const adapter of adapters) {
       adapterHashes.push(adapter.getHashableState());
     }
 
-    const data: SerializableObject = {
+    const data: JsonObject = {
       extra,
       adapterHashes,
     };
@@ -253,18 +253,20 @@ export class CanvasCompositorModule extends CanvasModuleBase {
    * @param rect The region to include in the rasterized image
    * @param uploadOptions Options for uploading the image
    * @param compositingOptions Options for compositing the entities
+   * @param forceUpload If true, the image is always re-uploaded, returning a new image DTO
    * @returns A promise that resolves to the image DTO
    */
   getCompositeImageDTO = async (
     adapters: CanvasEntityAdapter[],
     rect: Rect,
-    uploadOptions: Pick<UploadOptions, 'is_intermediate' | 'metadata'>,
-    compositingOptions?: CompositingOptions
+    uploadOptions: Pick<UploadImageArg, 'is_intermediate' | 'metadata'>,
+    compositingOptions?: CompositingOptions,
+    forceUpload?: boolean
   ): Promise<ImageDTO> => {
     assert(rect.width > 0 && rect.height > 0, 'Unable to rasterize empty rect');
 
     const hash = this.getCompositeHash(adapters, { rect });
-    const cachedImageName = this.manager.cache.imageNameCache.get(hash);
+    const cachedImageName = forceUpload ? undefined : this.manager.cache.imageNameCache.get(hash);
 
     let imageDTO: ImageDTO | null = null;
 
@@ -295,12 +297,12 @@ export class CanvasCompositorModule extends CanvasModuleBase {
     this.$isUploading.set(true);
     const uploadResult = await withResultAsync(() =>
       uploadImage({
-        blob,
-        fileName: 'canvas-composite.png',
+        file: new File([blob], 'canvas-composite.png', { type: 'image/png' }),
         image_category: 'general',
         is_intermediate: uploadOptions.is_intermediate,
         board_id: uploadOptions.is_intermediate ? undefined : selectAutoAddBoardId(this.manager.store.getState()),
         metadata: uploadOptions.metadata,
+        withToast: false,
       })
     );
     this.$isUploading.set(false);
@@ -327,6 +329,7 @@ export class CanvasCompositorModule extends CanvasModuleBase {
     entityIdentifiers: T[],
     deleteMergedEntities: boolean
   ): Promise<ImageDTO | null> => {
+    toast({ id: 'MERGE_LAYERS_TOAST', title: t('controlLayers.mergingLayers'), withCount: false });
     if (entityIdentifiers.length <= 1) {
       this.log.warn({ entityIdentifiers }, 'Cannot merge less than 2 entities');
       return null;
@@ -349,7 +352,12 @@ export class CanvasCompositorModule extends CanvasModuleBase {
 
     if (result.isErr()) {
       this.log.error({ error: serializeError(result.error) }, 'Failed to merge selected entities');
-      toast({ title: t('controlLayers.mergeVisibleError'), status: 'error' });
+      toast({
+        id: 'MERGE_LAYERS_TOAST',
+        title: t('controlLayers.mergeVisibleError'),
+        status: 'error',
+        withCount: false,
+      });
       return null;
     }
 
@@ -381,7 +389,7 @@ export class CanvasCompositorModule extends CanvasModuleBase {
         assert<Equals<typeof type, never>>(false, 'Unsupported type for merge');
     }
 
-    toast({ title: t('controlLayers.mergeVisibleOk') });
+    toast({ id: 'MERGE_LAYERS_TOAST', title: t('controlLayers.mergeVisibleOk'), status: 'success', withCount: false });
 
     return result.value;
   };

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

@@ -1,7 +1,6 @@
 import type { Selector } from '@reduxjs/toolkit';
 import { createSelector } from '@reduxjs/toolkit';
 import type { RootState } from 'app/store/store';
-import type { SerializableObject } from 'common/types';
 import { deepClone } from 'common/util/deepClone';
 import type { CanvasEntityBufferObjectRenderer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityBufferObjectRenderer';
 import type { CanvasEntityFilterer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityFilterer';
@@ -37,6 +36,7 @@ import type { Logger } from 'roarr';
 import type { ImageDTO } from 'services/api/types';
 import stableHash from 'stable-hash';
 import { assert } from 'tsafe';
+import type { Jsonifiable, JsonObject } from 'type-fest';
 
 // Ideally, we'd type `adapter` as `CanvasEntityAdapterBase`, but the generics make this tricky. `CanvasEntityAdapter`
 // is a union of all entity adapters and is functionally identical to `CanvasEntityAdapterBase`. We'll need to do a
@@ -111,7 +111,7 @@ export abstract class CanvasEntityAdapterBase<
    *
    * This is used for caching.
    */
-  abstract getHashableState: () => SerializableObject;
+  abstract getHashableState: () => JsonObject;
 
   /**
    * Callbacks that are executed when the module is initialized.
@@ -566,7 +566,7 @@ export abstract class CanvasEntityAdapterBase<
    * Gets a hash of the entity's state, as provided by `getHashableState`. If `extra` is provided, it will be included in
    * the hash.
    */
-  hash = (extra?: SerializableObject): string => {
+  hash = (extra?: Jsonifiable): string => {
     const arg = {
       state: this.getHashableState(),
       extra,
@@ -614,8 +614,8 @@ export abstract class CanvasEntityAdapterBase<
       transformer: this.transformer.repr(),
       renderer: this.renderer.repr(),
       bufferRenderer: this.bufferRenderer.repr(),
-      segmentAnything: this.segmentAnything?.repr(),
-      filterer: this.filterer?.repr(),
+      segmentAnything: this.segmentAnything?.repr() ?? null,
+      filterer: this.filterer?.repr() ?? null,
       hasCache: this.$canvasCache.get() !== null,
       isLocked: this.$isLocked.get(),
       isDisabled: this.$isDisabled.get(),

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

@@ -1,4 +1,3 @@
-import type { SerializableObject } from 'common/types';
 import { CanvasEntityAdapterBase } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterBase';
 import { CanvasEntityBufferObjectRenderer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityBufferObjectRenderer';
 import { CanvasEntityFilterer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityFilterer';
@@ -9,6 +8,7 @@ import { CanvasSegmentAnythingModule } from 'features/controlLayers/konva/Canvas
 import type { CanvasControlLayerState, CanvasEntityIdentifier, Rect } from 'features/controlLayers/store/types';
 import type { GroupConfig } from 'konva/lib/Group';
 import { omit } from 'lodash-es';
+import type { JsonObject } from 'type-fest';
 
 export class CanvasEntityAdapterControlLayer extends CanvasEntityAdapterBase<
   CanvasControlLayerState,
@@ -77,7 +77,7 @@ export class CanvasEntityAdapterControlLayer extends CanvasEntityAdapterBase<
     return canvas;
   };
 
-  getHashableState = (): SerializableObject => {
+  getHashableState = (): JsonObject => {
     const keysToOmit: (keyof CanvasControlLayerState)[] = [
       'name',
       'controlAdapter',

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

@@ -1,4 +1,3 @@
-import type { SerializableObject } from 'common/types';
 import { CanvasEntityAdapterBase } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterBase';
 import { CanvasEntityBufferObjectRenderer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityBufferObjectRenderer';
 import { CanvasEntityObjectRenderer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityObjectRenderer';
@@ -7,6 +6,7 @@ import type { CanvasManager } from 'features/controlLayers/konva/CanvasManager';
 import type { CanvasEntityIdentifier, CanvasInpaintMaskState, Rect } from 'features/controlLayers/store/types';
 import type { GroupConfig } from 'konva/lib/Group';
 import { omit } from 'lodash-es';
+import type { JsonObject } from 'type-fest';
 
 export class CanvasEntityAdapterInpaintMask extends CanvasEntityAdapterBase<
   CanvasInpaintMaskState,
@@ -69,7 +69,7 @@ export class CanvasEntityAdapterInpaintMask extends CanvasEntityAdapterBase<
     }
   };
 
-  getHashableState = (): SerializableObject => {
+  getHashableState = (): JsonObject => {
     const keysToOmit: (keyof CanvasInpaintMaskState)[] = ['fill', 'name', 'opacity', 'isLocked'];
     return omit(this.state, keysToOmit);
   };

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

@@ -1,4 +1,3 @@
-import type { SerializableObject } from 'common/types';
 import { CanvasEntityAdapterBase } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityAdapterBase';
 import { CanvasEntityBufferObjectRenderer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityBufferObjectRenderer';
 import { CanvasEntityFilterer } from 'features/controlLayers/konva/CanvasEntity/CanvasEntityFilterer';
@@ -9,6 +8,7 @@ import { CanvasSegmentAnythingModule } from 'features/controlLayers/konva/Canvas
 import type { CanvasEntityIdentifier, CanvasRasterLayerState, Rect } from 'features/controlLayers/store/types';
 import type { GroupConfig } from 'konva/lib/Group';
 import { omit } from 'lodash-es';
+import type { JsonObject } from 'type-fest';
 
 export class CanvasEntityAdapterRasterLayer extends CanvasEntityAdapterBase<
   CanvasRasterLayerState,
@@ -70,7 +70,7 @@ export class CanvasEntityAdapterRasterLayer extends CanvasEntityAdapterBase<
     return canvas;
   };
 
-  getHashableState = (): SerializableObject => {
+  getHashableState = (): JsonObject => {
     const keysToOmit: (keyof CanvasRasterLayerState)[] = ['name', 'isLocked'];
     return omit(this.state, keysToOmit);
   };