ruff

invokeai/app/api/routers/model_manager.py

@@ -751,7 +751,7 @@ async def convert_model(
 
     with TemporaryDirectory(dir=ApiDependencies.invoker.services.configuration.models_path) as tmpdir:
         convert_path = pathlib.Path(tmpdir) / pathlib.Path(model_config.path).stem
-        converted_model = loader.load_model(model_config)
+        converted_model = loader.load_model(model_config, queue_id="default")
         # write the converted file to the convert path
         raw_model = converted_model.model
         assert hasattr(raw_model, "save_pretrained")

invokeai/app/api/sockets.py

@@ -31,6 +31,7 @@ from invokeai.app.services.events.events_common import (
     ModelInstallErrorEvent,
     ModelInstallStartedEvent,
     ModelLoadCompleteEvent,
+    ModelLoadEventBase,
     ModelLoadStartedEvent,
     QueueClearedEvent,
     QueueEventBase,
@@ -53,6 +54,13 @@ class BulkDownloadSubscriptionEvent(BaseModel):
     bulk_download_id: str
 
 
+class ModelLoadSubscriptionEvent(BaseModel):
+    """Event data for subscribing to the socket.io model loading room.
+    This is a pydantic model to ensure the data is in the correct format."""
+
+    queue_id: str
+
+
 QUEUE_EVENTS = {
     InvocationStartedEvent,
     InvocationProgressEvent,
@@ -69,8 +77,6 @@ MODEL_EVENTS = {
     DownloadErrorEvent,
     DownloadProgressEvent,
     DownloadStartedEvent,
-    ModelLoadStartedEvent,
-    ModelLoadCompleteEvent,
     ModelInstallDownloadProgressEvent,
     ModelInstallDownloadsCompleteEvent,
     ModelInstallStartedEvent,
@@ -79,6 +85,11 @@ MODEL_EVENTS = {
     ModelInstallErrorEvent,
 }
 
+MODEL_LOAD_EVENTS = {
+    ModelLoadStartedEvent,
+    ModelLoadCompleteEvent,
+}
+
 BULK_DOWNLOAD_EVENTS = {BulkDownloadStartedEvent, BulkDownloadCompleteEvent, BulkDownloadErrorEvent}
 
 
@@ -101,6 +112,7 @@ class SocketIO:
 
         register_events(QUEUE_EVENTS, self._handle_queue_event)
         register_events(MODEL_EVENTS, self._handle_model_event)
+        register_events(MODEL_LOAD_EVENTS, self._handle_model_load_event)
         register_events(BULK_DOWNLOAD_EVENTS, self._handle_bulk_image_download_event)
 
     async def _handle_sub_queue(self, sid: str, data: Any) -> None:
@@ -115,9 +127,18 @@ class SocketIO:
     async def _handle_unsub_bulk_download(self, sid: str, data: Any) -> None:
         await self._sio.leave_room(sid, BulkDownloadSubscriptionEvent(**data).bulk_download_id)
 
+    async def _handle_sub_model_load(self, sid: str, data: Any) -> None:
+        await self._sio.enter_room(sid, ModelLoadSubscriptionEvent(**data).queue_id)
+
+    async def _handle_unsub_model_load(self, sid: str, data: Any) -> None:
+        await self._sio.leave_room(sid, ModelLoadSubscriptionEvent(**data).queue_id)
+
     async def _handle_queue_event(self, event: FastAPIEvent[QueueEventBase]):
         await self._sio.emit(event=event[0], data=event[1].model_dump(mode="json"), room=event[1].queue_id)
 
+    async def _handle_model_load_event(self, event: FastAPIEvent[ModelLoadEventBase]) -> None:
+        await self._sio.emit(event=event[0], data=event[1].model_dump(mode="json"), room=event[1].queue_id)
+
     async def _handle_model_event(self, event: FastAPIEvent[ModelEventBase | DownloadEventBase]) -> None:
         await self._sio.emit(event=event[0], data=event[1].model_dump(mode="json"))
 

invokeai/app/invocations/compel.py

@@ -63,12 +63,12 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.models.load(self.clip.tokenizer)
-        text_encoder_info = context.models.load(self.clip.text_encoder)
+        tokenizer_info = context.models.load(self.clip.tokenizer, queue_id=context.util.get_queue_id())
+        text_encoder_info = context.models.load(self.clip.text_encoder, queue_id=context.util.get_queue_id())
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.clip.loras:
-                lora_info = context.models.load(lora.lora)
+                lora_info = context.models.load(lora.lora, queue_id=context.util.get_queue_id())
                 assert isinstance(lora_info.model, LoRAModelRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
@@ -95,7 +95,6 @@ class CompelInvocation(BaseInvocation):
                 ti_manager,
             ),
         ):
-            context.util.signal_progress("Building conditioning")
             assert isinstance(text_encoder, CLIPTextModel)
             assert isinstance(tokenizer, CLIPTokenizer)
             compel = Compel(
@@ -138,8 +137,8 @@ class SDXLPromptInvocationBase:
         lora_prefix: str,
         zero_on_empty: bool,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
-        tokenizer_info = context.models.load(clip_field.tokenizer)
-        text_encoder_info = context.models.load(clip_field.text_encoder)
+        tokenizer_info = context.models.load(clip_field.tokenizer, queue_id=context.util.get_queue_id())
+        text_encoder_info = context.models.load(clip_field.text_encoder, queue_id=context.util.get_queue_id())
 
         # return zero on empty
         if prompt == "" and zero_on_empty:
@@ -164,7 +163,7 @@ class SDXLPromptInvocationBase:
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in clip_field.loras:
-                lora_info = context.models.load(lora.lora)
+                lora_info = context.models.load(lora.lora, context.util.get_queue_id())
                 lora_model = lora_info.model
                 assert isinstance(lora_model, LoRAModelRaw)
                 yield (lora_model, lora.weight)
@@ -192,7 +191,6 @@ class SDXLPromptInvocationBase:
                 ti_manager,
             ),
         ):
-            context.util.signal_progress("Building conditioning")
             assert isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
             assert isinstance(tokenizer, CLIPTokenizer)
 

invokeai/app/invocations/controlnet_image_processors.py

@@ -649,7 +649,9 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
             return DepthAnythingPipeline(depth_anything_pipeline)
 
         with self._context.models.load_remote_model(
-            source=DEPTH_ANYTHING_MODELS[self.model_size], loader=load_depth_anything
+            source=DEPTH_ANYTHING_MODELS[self.model_size],
+            queue_id=self._context.util.get_queue_id(),
+            loader=load_depth_anything,
         ) as depth_anything_detector:
             assert isinstance(depth_anything_detector, DepthAnythingPipeline)
             depth_map = depth_anything_detector.generate_depth(image)

invokeai/app/invocations/create_denoise_mask.py

@@ -60,12 +60,11 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         )
 
         if image_tensor is not None:
-            vae_info = context.models.load(self.vae.vae)
+            vae_info = context.models.load(self.vae.vae, context.util.get_queue_id())
 
             img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
             masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
             # TODO:
-            context.util.signal_progress("Running VAE encoder")
             masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
 
             masked_latents_name = context.tensors.save(tensor=masked_latents)

invokeai/app/invocations/create_gradient_mask.py

@@ -124,14 +124,13 @@ class CreateGradientMaskInvocation(BaseInvocation):
             assert isinstance(main_model_config, MainConfigBase)
             if main_model_config.variant is ModelVariantType.Inpaint:
                 mask = blur_tensor
-                vae_info: LoadedModel = context.models.load(self.vae.vae)
+                vae_info: LoadedModel = context.models.load(self.vae.vae, context.util.get_queue_id())
                 image = context.images.get_pil(self.image.image_name)
                 image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
                 if image_tensor.dim() == 3:
                     image_tensor = image_tensor.unsqueeze(0)
                 img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
                 masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
-                context.util.signal_progress("Running VAE encoder")
                 masked_latents = ImageToLatentsInvocation.vae_encode(
                     vae_info, self.fp32, self.tiled, masked_image.clone()
                 )

invokeai/app/invocations/denoise_latents.py

@@ -88,7 +88,7 @@ def get_scheduler(
     # TODO(ryand): Silently falling back to ddim seems like a bad idea. Look into why this was added and remove if
     # possible.
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
-    orig_scheduler_info = context.models.load(scheduler_info)
+    orig_scheduler_info = context.models.load(scheduler_info, context.util.get_queue_id())
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -435,7 +435,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         controlnet_data: list[ControlNetData] = []
         for control_info in control_list:
-            control_model = exit_stack.enter_context(context.models.load(control_info.control_model))
+            control_model = exit_stack.enter_context(
+                context.models.load(control_info.control_model, context.util.get_queue_id())
+            )
             assert isinstance(control_model, ControlNetModel)
 
             control_image_field = control_info.image
@@ -492,7 +494,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
             raise ValueError(f"Unexpected control_input type: {type(control_input)}")
 
         for control_info in control_list:
-            model = exit_stack.enter_context(context.models.load(control_info.control_model))
+            model = exit_stack.enter_context(
+                context.models.load(control_info.control_model, context.util.get_queue_id())
+            )
             ext_manager.add_extension(
                 ControlNetExt(
                     model=model,
@@ -545,9 +549,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
         """Run the IPAdapter CLIPVisionModel, returning image prompt embeddings."""
         image_prompts = []
         for single_ip_adapter in ip_adapters:
-            with context.models.load(single_ip_adapter.ip_adapter_model) as ip_adapter_model:
+            with context.models.load(
+                single_ip_adapter.ip_adapter_model, context.util.get_queue_id()
+            ) as ip_adapter_model:
                 assert isinstance(ip_adapter_model, IPAdapter)
-                image_encoder_model_info = context.models.load(single_ip_adapter.image_encoder_model)
+                image_encoder_model_info = context.models.load(
+                    single_ip_adapter.image_encoder_model, context.util.get_queue_id()
+                )
                 # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
                 single_ipa_image_fields = single_ip_adapter.image
                 if not isinstance(single_ipa_image_fields, list):
@@ -581,7 +589,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
         for single_ip_adapter, (image_prompt_embeds, uncond_image_prompt_embeds) in zip(
             ip_adapters, image_prompts, strict=True
         ):
-            ip_adapter_model = exit_stack.enter_context(context.models.load(single_ip_adapter.ip_adapter_model))
+            ip_adapter_model = exit_stack.enter_context(
+                context.models.load(single_ip_adapter.ip_adapter_model, context.util.get_queue_id())
+            )
 
             mask_field = single_ip_adapter.mask
             mask = context.tensors.load(mask_field.tensor_name) if mask_field is not None else None
@@ -621,7 +631,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
             t2i_adapter_model_config = context.models.get_config(t2i_adapter_field.t2i_adapter_model.key)
-            t2i_adapter_loaded_model = context.models.load(t2i_adapter_field.t2i_adapter_model)
+            t2i_adapter_loaded_model = context.models.load(
+                t2i_adapter_field.t2i_adapter_model, context.util.get_queue_id()
+            )
             image = context.images.get_pil(t2i_adapter_field.image.image_name, mode="RGB")
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
@@ -926,7 +938,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             # ext: t2i/ip adapter
             ext_manager.run_callback(ExtensionCallbackType.SETUP, denoise_ctx)
 
-            unet_info = context.models.load(self.unet.unet)
+            unet_info = context.models.load(self.unet.unet, context.util.get_queue_id())
             assert isinstance(unet_info.model, UNet2DConditionModel)
             with (
                 unet_info.model_on_device() as (cached_weights, unet),
@@ -989,13 +1001,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.unet.loras:
-                lora_info = context.models.load(lora.lora)
+                lora_info = context.models.load(lora.lora, context.util.get_queue_id())
                 assert isinstance(lora_info.model, LoRAModelRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
             return
 
-        unet_info = context.models.load(self.unet.unet)
+        unet_info = context.models.load(self.unet.unet, context.util.get_queue_id())
         assert isinstance(unet_info.model, UNet2DConditionModel)
         with (
             ExitStack() as exit_stack,

invokeai/app/invocations/depth_anything.py

@@ -35,7 +35,9 @@ class DepthAnythingDepthEstimationInvocation(BaseInvocation, WithMetadata, WithB
         model_url = DEPTH_ANYTHING_MODELS[self.model_size]
         image = context.images.get_pil(self.image.image_name, "RGB")
 
-        loaded_model = context.models.load_remote_model(model_url, DepthAnythingPipeline.load_model)
+        loaded_model = context.models.load_remote_model(
+            model_url, context.util.get_queue_id(), DepthAnythingPipeline.load_model
+        )
 
         with loaded_model as depth_anything_detector:
             assert isinstance(depth_anything_detector, DepthAnythingPipeline)

invokeai/app/invocations/dw_openpose.py

@@ -29,10 +29,10 @@ class DWOpenposeDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
         onnx_pose_path = context.models.download_and_cache_model(DWOpenposeDetector2.get_model_url_pose())
 
         loaded_session_det = context.models.load_local_model(
-            onnx_det_path, DWOpenposeDetector2.create_onnx_inference_session
+            onnx_det_path, context.util.get_queue_id(), DWOpenposeDetector2.create_onnx_inference_session
         )
         loaded_session_pose = context.models.load_local_model(
-            onnx_pose_path, DWOpenposeDetector2.create_onnx_inference_session
+            onnx_pose_path, context.util.get_queue_id(), DWOpenposeDetector2.create_onnx_inference_session
         )
 
         with loaded_session_det as session_det, loaded_session_pose as session_pose:

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="3.2.1",
+    version="3.2.0",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -81,7 +81,6 @@ 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,
@@ -184,7 +183,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             seed=self.seed,
         )
 
-        transformer_info = context.models.load(self.transformer.transformer)
+        transformer_info = context.models.load(self.transformer.transformer, context.util.get_queue_id())
         is_schnell = "schnell" in transformer_info.config.config_path
 
         # Calculate the timestep schedule.
@@ -208,12 +207,9 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                     "to be poor. Consider using a FLUX dev model instead."
                 )
 
-            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
+            # Noise the orig_latents by the appropriate amount for the first timestep.
+            t_0 = timesteps[0]
+            x = t_0 * noise + (1.0 - t_0) * init_latents
         else:
             # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
             if self.denoising_start > 1e-5:
@@ -472,7 +468,9 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # minimize peak memory.
 
         # First, load the ControlNet models so that we can determine the ControlNet types.
-        controlnet_models = [context.models.load(controlnet.control_model) for controlnet in controlnets]
+        controlnet_models = [
+            context.models.load(controlnet.control_model, context.util.get_queue_id()) for controlnet in controlnets
+        ]
 
         # Calculate the controlnet conditioning tensors.
         # We do this before loading the ControlNet models because it may require running the VAE, and we are trying to
@@ -483,7 +481,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             if isinstance(controlnet_model.model, InstantXControlNetFlux):
                 if self.controlnet_vae is None:
                     raise ValueError("A ControlNet VAE is required when using an InstantX FLUX ControlNet.")
-                vae_info = context.models.load(self.controlnet_vae.vae)
+                vae_info = context.models.load(self.controlnet_vae.vae, context.util.get_queue_id())
                 controlnet_conds.append(
                     InstantXControlNetExtension.prepare_controlnet_cond(
                         controlnet_image=image,
@@ -594,7 +592,9 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 pos_images.append(pos_image)
                 neg_images.append(neg_image)
 
-            with context.models.load(ip_adapter_field.image_encoder_model) as image_encoder_model:
+            with context.models.load(
+                ip_adapter_field.image_encoder_model, context.util.get_queue_id()
+            ) as image_encoder_model:
                 assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
 
                 clip_image: torch.Tensor = clip_image_processor(images=pos_images, return_tensors="pt").pixel_values
@@ -624,7 +624,9 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         for ip_adapter_field, pos_image_prompt_clip_embed, neg_image_prompt_clip_embed in zip(
             ip_adapter_fields, pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds, strict=True
         ):
-            ip_adapter_model = exit_stack.enter_context(context.models.load(ip_adapter_field.ip_adapter_model))
+            ip_adapter_model = exit_stack.enter_context(
+                context.models.load(ip_adapter_field.ip_adapter_model, context.util.get_queue_id())
+            )
             assert isinstance(ip_adapter_model, XlabsIpAdapterFlux)
             ip_adapter_model = ip_adapter_model.to(dtype=dtype)
             if ip_adapter_field.mask is not None:
@@ -653,7 +655,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
         for lora in self.transformer.loras:
-            lora_info = context.models.load(lora.lora)
+            lora_info = context.models.load(lora.lora, context.util.get_queue_id())
             assert isinstance(lora_info.model, LoRAModelRaw)
             yield (lora_info.model, lora.weight)
             del lora_info

invokeai/app/invocations/flux_text_encoder.py

@@ -57,8 +57,8 @@ class FluxTextEncoderInvocation(BaseInvocation):
         return FluxConditioningOutput.build(conditioning_name)
 
     def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
-        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
-        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
+        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer, context.util.get_queue_id())
+        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder, context.util.get_queue_id())
 
         prompt = [self.prompt]
 
@@ -71,15 +71,14 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
 
-            context.util.signal_progress("Running T5 encoder")
             prompt_embeds = t5_encoder(prompt)
 
         assert isinstance(prompt_embeds, torch.Tensor)
         return prompt_embeds
 
     def _clip_encode(self, context: InvocationContext) -> torch.Tensor:
-        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
-        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
+        clip_tokenizer_info = context.models.load(self.clip.tokenizer, context.util.get_queue_id())
+        clip_text_encoder_info = context.models.load(self.clip.text_encoder, context.util.get_queue_id())
 
         prompt = [self.prompt]
 
@@ -112,7 +111,6 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
 
-            context.util.signal_progress("Running CLIP encoder")
             pooled_prompt_embeds = clip_encoder(prompt)
 
         assert isinstance(pooled_prompt_embeds, torch.Tensor)
@@ -120,7 +118,7 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
     def _clip_lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
         for lora in self.clip.loras:
-            lora_info = context.models.load(lora.lora)
+            lora_info = context.models.load(lora.lora, context.util.get_queue_id())
             assert isinstance(lora_info.model, LoRAModelRaw)
             yield (lora_info.model, lora.weight)
             del lora_info

invokeai/app/invocations/flux_vae_decode.py

@@ -41,8 +41,7 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
         with vae_info as vae:
             assert isinstance(vae, AutoEncoder)
-            vae_dtype = next(iter(vae.parameters())).dtype
-            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
+            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype())
             img = vae.decode(latents)
 
         img = img.clamp(-1, 1)
@@ -53,8 +52,7 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     @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)
-        context.util.signal_progress("Running VAE")
+        vae_info = context.models.load(self.vae.vae, context.util.get_queue_id())
         image = self._vae_decode(vae_info=vae_info, latents=latents)
 
         TorchDevice.empty_cache()

invokeai/app/invocations/flux_vae_encode.py

@@ -44,8 +44,9 @@ class FluxVaeEncodeInvocation(BaseInvocation):
         generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
         with vae_info as vae:
             assert isinstance(vae, AutoEncoder)
-            vae_dtype = next(iter(vae.parameters())).dtype
-            image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
+            image_tensor = image_tensor.to(
+                device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
+            )
             latents = vae.encode(image_tensor, sample=True, generator=generator)
             return latents
 
@@ -53,13 +54,12 @@ class FluxVaeEncodeInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         image = context.images.get_pil(self.image.image_name)
 
-        vae_info = context.models.load(self.vae.vae)
+        vae_info = context.models.load(self.vae.vae, context.util.get_queue_id())
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
-        context.util.signal_progress("Running VAE")
         latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")

invokeai/app/invocations/grounding_dino.py

@@ -94,7 +94,9 @@ class GroundingDinoInvocation(BaseInvocation):
         labels = [label if label.endswith(".") else label + "." for label in labels]
 
         with context.models.load_remote_model(
-            source=GROUNDING_DINO_MODEL_IDS[self.model], loader=GroundingDinoInvocation._load_grounding_dino
+            source=GROUNDING_DINO_MODEL_IDS[self.model],
+            queue_id=context.util.get_queue_id(),
+            loader=GroundingDinoInvocation._load_grounding_dino,
         ) as detector:
             assert isinstance(detector, GroundingDinoPipeline)
             return detector.detect(image=image, candidate_labels=labels, threshold=threshold)

invokeai/app/invocations/hed.py

@@ -22,7 +22,9 @@ class HEDEdgeDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.images.get_pil(self.image.image_name, "RGB")
-        loaded_model = context.models.load_remote_model(HEDEdgeDetector.get_model_url(), HEDEdgeDetector.load_model)
+        loaded_model = context.models.load_remote_model(
+            HEDEdgeDetector.get_model_url(), context.util.get_queue_id(), HEDEdgeDetector.load_model
+        )
 
         with loaded_model as model:
             assert isinstance(model, ControlNetHED_Apache2)

invokeai/app/invocations/image_to_latents.py

@@ -111,13 +111,12 @@ class ImageToLatentsInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         image = context.images.get_pil(self.image.image_name)
 
-        vae_info = context.models.load(self.vae.vae)
+        vae_info = context.models.load(self.vae.vae, context.util.get_queue_id())
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
-        context.util.signal_progress("Running VAE encoder")
         latents = self.vae_encode(
             vae_info=vae_info, upcast=self.fp32, tiled=self.tiled, image_tensor=image_tensor, tile_size=self.tile_size
         )

invokeai/app/invocations/infill.py

@@ -36,7 +36,7 @@ class InfillImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
     image: ImageField = InputField(description="The image to process")
 
     @abstractmethod
-    def infill(self, image: Image.Image) -> Image.Image:
+    def infill(self, image: Image.Image, queue_id: str) -> Image.Image:
         """Infill the image with the specified method"""
         pass
 
@@ -56,7 +56,7 @@ class InfillImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
             return ImageOutput.build(context.images.get_dto(self.image.image_name))
 
         # Perform Infill action
-        infilled_image = self.infill(input_image)
+        infilled_image = self.infill(input_image, context.util.get_queue_id())
 
         # Create ImageDTO for Infilled Image
         infilled_image_dto = context.images.save(image=infilled_image)
@@ -74,7 +74,7 @@ class InfillColorInvocation(InfillImageProcessorInvocation):
         description="The color to use to infill",
     )
 
-    def infill(self, image: Image.Image):
+    def infill(self, image: Image.Image, queue_id: str):
         solid_bg = Image.new("RGBA", image.size, self.color.tuple())
         infilled = Image.alpha_composite(solid_bg, image.convert("RGBA"))
         infilled.paste(image, (0, 0), image.split()[-1])
@@ -93,7 +93,7 @@ class InfillTileInvocation(InfillImageProcessorInvocation):
         description="The seed to use for tile generation (omit for random)",
     )
 
-    def infill(self, image: Image.Image):
+    def infill(self, image: Image.Image, queue_id: str):
         output = infill_tile(image, seed=self.seed, tile_size=self.tile_size)
         return output.infilled
 
@@ -107,7 +107,7 @@ class InfillPatchMatchInvocation(InfillImageProcessorInvocation):
     downscale: float = InputField(default=2.0, gt=0, description="Run patchmatch on downscaled image to speedup infill")
     resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
 
-    def infill(self, image: Image.Image):
+    def infill(self, image: Image.Image, queue_id: str):
         resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
 
         width = int(image.width / self.downscale)
@@ -131,9 +131,10 @@ class InfillPatchMatchInvocation(InfillImageProcessorInvocation):
 class LaMaInfillInvocation(InfillImageProcessorInvocation):
     """Infills transparent areas of an image using the LaMa model"""
 
-    def infill(self, image: Image.Image):
+    def infill(self, image: Image.Image, queue_id: str):
         with self._context.models.load_remote_model(
             source="https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
+            queue_id=queue_id,
             loader=LaMA.load_jit_model,
         ) as model:
             lama = LaMA(model)
@@ -144,7 +145,7 @@ class LaMaInfillInvocation(InfillImageProcessorInvocation):
 class CV2InfillInvocation(InfillImageProcessorInvocation):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
-    def infill(self, image: Image.Image):
+    def infill(self, image: Image.Image, queue_id: str):
         return cv2_inpaint(image)
 
 
@@ -166,5 +167,5 @@ class MosaicInfillInvocation(InfillImageProcessorInvocation):
         description="The max threshold for color",
     )
 
-    def infill(self, image: Image.Image):
+    def infill(self, image: Image.Image, queue_id: str):
         return infill_mosaic(image, (self.tile_width, self.tile_height), self.min_color.tuple(), self.max_color.tuple())

invokeai/app/invocations/latents_to_image.py

@@ -57,10 +57,9 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
-        vae_info = context.models.load(self.vae.vae)
+        vae_info = context.models.load(self.vae.vae, context.util.get_queue_id())
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
         with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
-            context.util.signal_progress("Running VAE decoder")
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             latents = latents.to(vae.device)
             if self.fp32:

invokeai/app/invocations/lineart.py

@@ -23,7 +23,9 @@ class LineartEdgeDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.images.get_pil(self.image.image_name, "RGB")
         model_url = LineartEdgeDetector.get_model_url(self.coarse)
-        loaded_model = context.models.load_remote_model(model_url, LineartEdgeDetector.load_model)
+        loaded_model = context.models.load_remote_model(
+            model_url, context.util.get_queue_id(), LineartEdgeDetector.load_model
+        )
 
         with loaded_model as model:
             assert isinstance(model, Generator)

invokeai/app/invocations/lineart_anime.py

@@ -20,7 +20,9 @@ class LineartAnimeEdgeDetectionInvocation(BaseInvocation, WithMetadata, WithBoar
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.images.get_pil(self.image.image_name, "RGB")
         model_url = LineartAnimeEdgeDetector.get_model_url()
-        loaded_model = context.models.load_remote_model(model_url, LineartAnimeEdgeDetector.load_model)
+        loaded_model = context.models.load_remote_model(
+            model_url, context.util.get_queue_id(), LineartAnimeEdgeDetector.load_model
+        )
 
         with loaded_model as model:
             assert isinstance(model, UnetGenerator)

invokeai/app/invocations/metadata.py

@@ -147,10 +147,6 @@ GENERATION_MODES = Literal[
     "flux_img2img",
     "flux_inpaint",
     "flux_outpaint",
-    "sd3_txt2img",
-    "sd3_img2img",
-    "sd3_inpaint",
-    "sd3_outpaint",
 ]
 
 

invokeai/app/invocations/mlsd.py

@@ -28,7 +28,9 @@ class MLSDDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.images.get_pil(self.image.image_name, "RGB")
-        loaded_model = context.models.load_remote_model(MLSDDetector.get_model_url(), MLSDDetector.load_model)
+        loaded_model = context.models.load_remote_model(
+            MLSDDetector.get_model_url(), context.util.get_queue_id(), MLSDDetector.load_model
+        )
 
         with loaded_model as model:
             assert isinstance(model, MobileV2_MLSD_Large)

invokeai/app/invocations/normal_bae.py

@@ -20,7 +20,9 @@ class NormalMapInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.images.get_pil(self.image.image_name, "RGB")
-        loaded_model = context.models.load_remote_model(NormalMapDetector.get_model_url(), NormalMapDetector.load_model)
+        loaded_model = context.models.load_remote_model(
+            NormalMapDetector.get_model_url(), context.util.get_queue_id(), NormalMapDetector.load_model
+        )
 
         with loaded_model as model:
             assert isinstance(model, NNET)

invokeai/app/invocations/pidi.py

@@ -22,7 +22,9 @@ class PiDiNetEdgeDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.images.get_pil(self.image.image_name, "RGB")
-        loaded_model = context.models.load_remote_model(PIDINetDetector.get_model_url(), PIDINetDetector.load_model)
+        loaded_model = context.models.load_remote_model(
+            PIDINetDetector.get_model_url(), context.util.get_queue_id(), PIDINetDetector.load_model
+        )
 
         with loaded_model as model:
             assert isinstance(model, PiDiNet)

invokeai/app/invocations/sd3_denoise.py

@@ -1,19 +1,16 @@
-from typing import Callable, Optional, Tuple
+from typing import Callable, Tuple
 
 import torch
-import torchvision.transforms as tv_transforms
 from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
-from torchvision.transforms.functional import resize as tv_resize
+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 (
-    DenoiseMaskField,
     FieldDescriptions,
     Input,
     InputField,
-    LatentsField,
     SD3ConditioningField,
     WithBoard,
     WithMetadata,
@@ -22,9 +19,7 @@ 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.flux.sampling_utils import clip_timestep_schedule_fractional
 from invokeai.backend.model_manager.config import BaseModelType
-from invokeai.backend.sd3.extensions.inpaint_extension import InpaintExtension
 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
@@ -35,24 +30,16 @@ from invokeai.backend.util.devices import TorchDevice
     title="SD3 Denoise",
     tags=["image", "sd3"],
     category="image",
-    version="1.1.0",
+    version="1.0.0",
     classification=Classification.Prototype,
 )
 class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Run denoising process with a SD3 model."""
 
-    # If latents is provided, this means we are doing image-to-image.
-    latents: Optional[LatentsField] = InputField(
-        default=None, description=FieldDescriptions.latents, input=Input.Connection
-    )
-    # denoise_mask is used for image-to-image inpainting. Only the masked region is modified.
-    denoise_mask: Optional[DenoiseMaskField] = InputField(
-        default=None, description=FieldDescriptions.denoise_mask, input=Input.Connection
-    )
-    denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start)
-    denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
     transformer: TransformerField = InputField(
-        description=FieldDescriptions.sd3_model, input=Input.Connection, title="Transformer"
+        description=FieldDescriptions.sd3_model,
+        input=Input.Connection,
+        title="Transformer",
     )
     positive_conditioning: SD3ConditioningField = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection
@@ -74,41 +61,6 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         name = context.tensors.save(tensor=latents)
         return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
-    def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
-        """Prepare the inpaint mask.
-        - Loads the mask
-        - Resizes if necessary
-        - Casts to same device/dtype as latents
-
-        Args:
-            context (InvocationContext): The invocation context, for loading the inpaint mask.
-            latents (torch.Tensor): A latent image tensor. Used to determine the target shape, device, and dtype for the
-                inpaint mask.
-
-        Returns:
-            torch.Tensor | None: Inpaint mask. Values of 0.0 represent the regions to be fully denoised, and 1.0
-                represent the regions to be preserved.
-        """
-        if self.denoise_mask is None:
-            return None
-        mask = context.tensors.load(self.denoise_mask.mask_name)
-
-        # The input denoise_mask contains values in [0, 1], where 0.0 represents the regions to be fully denoised, and
-        # 1.0 represents the regions to be preserved.
-        # We invert the mask so that the regions to be preserved are 0.0 and the regions to be denoised are 1.0.
-        mask = 1.0 - mask
-
-        _, _, latent_height, latent_width = latents.shape
-        mask = tv_resize(
-            img=mask,
-            size=[latent_height, latent_width],
-            interpolation=tv_transforms.InterpolationMode.BILINEAR,
-            antialias=False,
-        )
-
-        mask = mask.to(device=latents.device, dtype=latents.dtype)
-        return mask
-
     def _load_text_conditioning(
         self,
         context: InvocationContext,
@@ -195,7 +147,7 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         inference_dtype = TorchDevice.choose_torch_dtype()
         device = TorchDevice.choose_torch_device()
 
-        transformer_info = context.models.load(self.transformer.transformer)
+        transformer_info = context.models.load(self.transformer.transformer, context.util.get_queue_id())
 
         # Load/process the conditioning data.
         # TODO(ryand): Make CFG optional.
@@ -218,20 +170,14 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         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 timestep schedule.
-        # We add an extra step to the end to account for the final timestep of 0.0.
-        timesteps: list[float] = torch.linspace(1, 0, self.steps + 1).tolist()
-        # Clip the timesteps schedule based on denoising_start and denoising_end.
-        timesteps = clip_timestep_schedule_fractional(timesteps, self.denoising_start, self.denoising_end)
-        total_steps = len(timesteps) - 1
+        # 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(total_steps)
-
-        # Load the input latents, if provided.
-        init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
-        if init_latents is not None:
-            init_latents = init_latents.to(device=device, dtype=inference_dtype)
+        cfg_scale = self._prepare_cfg_scale(len(timesteps))
 
         # Generate initial latent noise.
         num_channels_latents = transformer_info.model.config.in_channels
@@ -245,34 +191,9 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             device=device,
             seed=self.seed,
         )
+        latents: torch.Tensor = noise
 
-        # Prepare input latent image.
-        if init_latents is not None:
-            # Noise the init_latents by the appropriate amount for the first timestep.
-            t_0 = timesteps[0]
-            latents = t_0 * noise + (1.0 - t_0) * init_latents
-        else:
-            # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
-            if self.denoising_start > 1e-5:
-                raise ValueError("denoising_start should be 0 when initial latents are not provided.")
-            latents = noise
-
-        # If len(timesteps) == 1, then short-circuit. We are just noising the input latents, but not taking any
-        # denoising steps.
-        if len(timesteps) <= 1:
-            return latents
-
-        # Prepare inpaint extension.
-        inpaint_mask = self._prep_inpaint_mask(context, latents)
-        inpaint_extension: InpaintExtension | None = None
-        if inpaint_mask is not None:
-            assert init_latents is not None
-            inpaint_extension = InpaintExtension(
-                init_latents=init_latents,
-                inpaint_mask=inpaint_mask,
-                noise=noise,
-            )
-
+        total_steps = len(timesteps)
         step_callback = self._build_step_callback(context)
 
         step_callback(
@@ -289,12 +210,11 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             assert isinstance(transformer, SD3Transformer2DModel)
 
             # 6. Denoising loop
-            for step_idx, (t_curr, t_prev) in tqdm(list(enumerate(zip(timesteps[:-1], timesteps[1:], strict=True)))):
+            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.
-                # Multiply by 1000 to match the default FlowMatchEulerDiscreteScheduler num_train_timesteps.
-                timestep = torch.tensor([t_curr * 1000], device=device).expand(latent_model_input.shape[0])
+                timestep = t.expand(latent_model_input.shape[0])
 
                 noise_pred = transformer(
                     hidden_states=latent_model_input,
@@ -312,19 +232,21 @@ class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
                 # Compute the previous noisy sample x_t -> x_t-1.
                 latents_dtype = latents.dtype
-                latents = latents.to(dtype=torch.float32)
-                latents = latents + (t_prev - t_curr) * noise_pred
-                latents = latents.to(dtype=latents_dtype)
+                latents = scheduler.step(model_output=noise_pred, timestep=t, sample=latents, return_dict=False)[0]
 
-                if inpaint_extension is not None:
-                    latents = inpaint_extension.merge_intermediate_latents_with_init_latents(latents, t_prev)
+                # 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_curr),
+                        timestep=int(t),
                         latents=latents,
                     ),
                 )

invokeai/app/invocations/sd3_image_to_latents.py

@@ -1,65 +0,0 @@
-import einops
-import torch
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    ImageField,
-    Input,
-    InputField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import VAEField
-from invokeai.app.invocations.primitives import LatentsOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.load.load_base import LoadedModel
-from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
-
-
-@invocation(
-    "sd3_i2l",
-    title="SD3 Image to Latents",
-    tags=["image", "latents", "vae", "i2l", "sd3"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Generates latents from an image."""
-
-    image: ImageField = InputField(description="The image to encode")
-    vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
-
-    @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        with vae_info as vae:
-            assert isinstance(vae, AutoencoderKL)
-
-            vae.disable_tiling()
-
-            image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
-            with torch.inference_mode():
-                image_tensor_dist = vae.encode(image_tensor).latent_dist
-                # TODO: Use seed to make sampling reproducible.
-                latents: torch.Tensor = image_tensor_dist.sample().to(dtype=vae.dtype)
-
-            latents = vae.config.scaling_factor * latents
-
-        return latents
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        image = context.images.get_pil(self.image.image_name)
-
-        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
-        if image_tensor.dim() == 3:
-            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
-
-        vae_info = context.models.load(self.vae.vae)
-        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
-
-        latents = latents.to("cpu")
-        name = context.tensors.save(tensor=latents)
-        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)

invokeai/app/invocations/sd3_latents_to_image.py

@@ -44,10 +44,9 @@ class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
-        vae_info = context.models.load(self.vae.vae)
+        vae_info = context.models.load(self.vae.vae, context.util.get_queue_id())
         assert isinstance(vae_info.model, (AutoencoderKL))
         with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
-            context.util.signal_progress("Running VAE")
             assert isinstance(vae, (AutoencoderKL))
             latents = latents.to(vae.device)
 

invokeai/app/invocations/sd3_text_encoder.py

@@ -86,8 +86,8 @@ class Sd3TextEncoderInvocation(BaseInvocation):
 
     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)
+        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer, context.util.get_queue_id())
+        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder, context.util.get_queue_id())
 
         prompt = [self.prompt]
 
@@ -95,7 +95,6 @@ class Sd3TextEncoderInvocation(BaseInvocation):
             t5_text_encoder_info as t5_text_encoder,
             t5_tokenizer_info as t5_tokenizer,
         ):
-            context.util.signal_progress("Running T5 encoder")
             assert isinstance(t5_text_encoder, T5EncoderModel)
             assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast))
 
@@ -128,8 +127,8 @@ class Sd3TextEncoderInvocation(BaseInvocation):
     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)
+        clip_tokenizer_info = context.models.load(clip_model.tokenizer, context.util.get_queue_id())
+        clip_text_encoder_info = context.models.load(clip_model.text_encoder, context.util.get_queue_id())
 
         prompt = [self.prompt]
 
@@ -138,7 +137,6 @@ class Sd3TextEncoderInvocation(BaseInvocation):
             clip_tokenizer_info as clip_tokenizer,
             ExitStack() as exit_stack,
         ):
-            context.util.signal_progress("Running CLIP encoder")
             assert isinstance(clip_text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
             assert isinstance(clip_tokenizer, CLIPTokenizer)
 
@@ -195,7 +193,7 @@ class Sd3TextEncoderInvocation(BaseInvocation):
         self, context: InvocationContext, clip_model: CLIPField
     ) -> Iterator[Tuple[LoRAModelRaw, float]]:
         for lora in clip_model.loras:
-            lora_info = context.models.load(lora.lora)
+            lora_info = context.models.load(lora.lora, context.util.get_queue_id())
             assert isinstance(lora_info.model, LoRAModelRaw)
             yield (lora_info.model, lora.weight)
             del lora_info

invokeai/app/invocations/segment_anything.py

@@ -125,7 +125,9 @@ class SegmentAnythingInvocation(BaseInvocation):
 
         with (
             context.models.load_remote_model(
-                source=SEGMENT_ANYTHING_MODEL_IDS[self.model], loader=SegmentAnythingInvocation._load_sam_model
+                source=SEGMENT_ANYTHING_MODEL_IDS[self.model],
+                queue_id=context.util.get_queue_id(),
+                loader=SegmentAnythingInvocation._load_sam_model,
             ) as sam_pipeline,
         ):
             assert isinstance(sam_pipeline, SegmentAnythingPipeline)

invokeai/app/invocations/spandrel_image_to_image.py

@@ -158,7 +158,7 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         image = context.images.get_pil(self.image.image_name, mode="RGB")
 
         # Load the model.
-        spandrel_model_info = context.models.load(self.image_to_image_model)
+        spandrel_model_info = context.models.load(self.image_to_image_model, context.util.get_queue_id())
 
         def step_callback(step: int, total_steps: int) -> None:
             context.util.signal_progress(
@@ -207,7 +207,7 @@ class SpandrelImageToImageAutoscaleInvocation(SpandrelImageToImageInvocation):
         image = context.images.get_pil(self.image.image_name, mode="RGB")
 
         # Load the model.
-        spandrel_model_info = context.models.load(self.image_to_image_model)
+        spandrel_model_info = context.models.load(self.image_to_image_model, context.util.get_queue_id())
 
         # The target size of the image, determined by the provided scale. We'll run the upscaler until we hit this size.
         # Later, we may mutate this value if the model doesn't upscale the image or if the user requested a multiple of 8.

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -196,13 +196,13 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
         # Prepare an iterator that yields the UNet's LoRA models and their weights.
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.unet.loras:
-                lora_info = context.models.load(lora.lora)
+                lora_info = context.models.load(lora.lora, context.util.get_queue_id())
                 assert isinstance(lora_info.model, LoRAModelRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
 
         # Load the UNet model.
-        unet_info = context.models.load(self.unet.unet)
+        unet_info = context.models.load(self.unet.unet, context.util.get_queue_id())
 
         with (
             ExitStack() as exit_stack,

invokeai/app/invocations/upscale.py

@@ -90,7 +90,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
             raise ValueError(msg)
 
         loadnet = context.models.load_remote_model(
-            source=ESRGAN_MODEL_URLS[self.model_name],
+            source=ESRGAN_MODEL_URLS[self.model_name], queue_id=context.util.get_queue_id()
         )
 
         with loadnet as loadnet_model:

invokeai/app/services/events/events_base.py

@@ -131,15 +131,17 @@ class EventServiceBase:
 
     # region Model loading
 
-    def emit_model_load_started(self, config: "AnyModelConfig", submodel_type: Optional["SubModelType"] = None) -> None:
+    def emit_model_load_started(
+        self, config: "AnyModelConfig", queue_id: str, submodel_type: Optional["SubModelType"] = None
+    ) -> None:
         """Emitted when a model load is started."""
-        self.dispatch(ModelLoadStartedEvent.build(config, submodel_type))
+        self.dispatch(ModelLoadStartedEvent.build(config, queue_id, submodel_type))
 
     def emit_model_load_complete(
-        self, config: "AnyModelConfig", submodel_type: Optional["SubModelType"] = None
+        self, config: "AnyModelConfig", queue_id: str, submodel_type: Optional["SubModelType"] = None
     ) -> None:
         """Emitted when a model load is complete."""
-        self.dispatch(ModelLoadCompleteEvent.build(config, submodel_type))
+        self.dispatch(ModelLoadCompleteEvent.build(config, queue_id, submodel_type))
 
     # endregion
 

invokeai/app/services/events/events_common.py

@@ -383,12 +383,14 @@ class DownloadErrorEvent(DownloadEventBase):
         return cls(source=str(job.source), error_type=job.error_type, error=job.error)
 
 
-class ModelEventBase(EventBase):
-    """Base class for events associated with a model"""
+class ModelLoadEventBase(EventBase):
+    """Base class for queue events"""
+
+    queue_id: str = Field(description="The ID of the queue")
 
 
 @payload_schema.register
-class ModelLoadStartedEvent(ModelEventBase):
+class ModelLoadStartedEvent(ModelLoadEventBase):
     """Event model for model_load_started"""
 
     __event_name__ = "model_load_started"
@@ -397,12 +399,14 @@ class ModelLoadStartedEvent(ModelEventBase):
     submodel_type: Optional[SubModelType] = Field(default=None, description="The submodel type, if any")
 
     @classmethod
-    def build(cls, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> "ModelLoadStartedEvent":
-        return cls(config=config, submodel_type=submodel_type)
+    def build(
+        cls, config: AnyModelConfig, queue_id: str, submodel_type: Optional[SubModelType] = None
+    ) -> "ModelLoadStartedEvent":
+        return cls(config=config, queue_id=queue_id, submodel_type=submodel_type)
 
 
 @payload_schema.register
-class ModelLoadCompleteEvent(ModelEventBase):
+class ModelLoadCompleteEvent(ModelLoadEventBase):
     """Event model for model_load_complete"""
 
     __event_name__ = "model_load_complete"
@@ -411,8 +415,14 @@ class ModelLoadCompleteEvent(ModelEventBase):
     submodel_type: Optional[SubModelType] = Field(default=None, description="The submodel type, if any")
 
     @classmethod
-    def build(cls, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> "ModelLoadCompleteEvent":
-        return cls(config=config, submodel_type=submodel_type)
+    def build(
+        cls, config: AnyModelConfig, queue_id: str, submodel_type: Optional[SubModelType] = None
+    ) -> "ModelLoadCompleteEvent":
+        return cls(config=config, queue_id=queue_id, submodel_type=submodel_type)
+
+
+class ModelEventBase(EventBase):
+    """Base class for model events"""
 
 
 @payload_schema.register

invokeai/app/services/model_load/model_load_base.py

@@ -14,7 +14,9 @@ class ModelLoadServiceBase(ABC):
     """Wrapper around AnyModelLoader."""
 
     @abstractmethod
-    def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
+    def load_model(
+        self, model_config: AnyModelConfig, queue_id: str, submodel_type: Optional[SubModelType] = None
+    ) -> LoadedModel:
         """
         Given a model's configuration, load it and return the LoadedModel object.
 
@@ -29,7 +31,7 @@ class ModelLoadServiceBase(ABC):
 
     @abstractmethod
     def load_model_from_path(
-        self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None
+        self, model_path: Path, queue_id: str, loader: Optional[Callable[[Path], AnyModel]] = None
     ) -> LoadedModelWithoutConfig:
         """
         Load the model file or directory located at the indicated Path.

invokeai/app/services/model_load/model_load_default.py

@@ -49,7 +49,9 @@ class ModelLoadService(ModelLoadServiceBase):
         """Return the RAM cache used by this loader."""
         return self._ram_cache
 
-    def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
+    def load_model(
+        self, model_config: AnyModelConfig, queue_id: str, submodel_type: Optional[SubModelType] = None
+    ) -> LoadedModel:
         """
         Given a model's configuration, load it and return the LoadedModel object.
 
@@ -60,7 +62,7 @@ class ModelLoadService(ModelLoadServiceBase):
         # We don't have an invoker during testing
         # TODO(psyche): Mock this method on the invoker in the tests
         if hasattr(self, "_invoker"):
-            self._invoker.services.events.emit_model_load_started(model_config, submodel_type)
+            self._invoker.services.events.emit_model_load_started(model_config, queue_id, submodel_type)
 
         implementation, model_config, submodel_type = self._registry.get_implementation(model_config, submodel_type)  # type: ignore
         loaded_model: LoadedModel = implementation(
@@ -70,12 +72,12 @@ class ModelLoadService(ModelLoadServiceBase):
         ).load_model(model_config, submodel_type)
 
         if hasattr(self, "_invoker"):
-            self._invoker.services.events.emit_model_load_complete(model_config, submodel_type)
+            self._invoker.services.events.emit_model_load_complete(model_config, queue_id, submodel_type)
 
         return loaded_model
 
     def load_model_from_path(
-        self, model_path: Path, loader: Optional[Callable[[Path], AnyModel]] = None
+        self, model_path: Path, queue_id: str, loader: Optional[Callable[[Path], AnyModel]] = None
     ) -> LoadedModelWithoutConfig:
         cache_key = str(model_path)
         ram_cache = self.ram_cache

invokeai/app/services/shared/invocation_context.py

@@ -160,10 +160,6 @@ class LoggerInterface(InvocationContextInterface):
 
 
 class ImagesInterface(InvocationContextInterface):
-    def __init__(self, services: InvocationServices, data: InvocationContextData, util: "UtilInterface") -> None:
-        super().__init__(services, data)
-        self._util = util
-
     def save(
         self,
         image: Image,
@@ -190,8 +186,6 @@ class ImagesInterface(InvocationContextInterface):
             The saved image DTO.
         """
 
-        self._util.signal_progress("Saving image")
-
         # If `metadata` is provided directly, use that. Else, use the metadata provided by `WithMetadata`, falling back to None.
         metadata_ = None
         if metadata:
@@ -342,10 +336,6 @@ class ConditioningInterface(InvocationContextInterface):
 class ModelsInterface(InvocationContextInterface):
     """Common API for loading, downloading and managing models."""
 
-    def __init__(self, services: InvocationServices, data: InvocationContextData, util: "UtilInterface") -> None:
-        super().__init__(services, data)
-        self._util = util
-
     def exists(self, identifier: Union[str, "ModelIdentifierField"]) -> bool:
         """Check if a model exists.
 
@@ -361,7 +351,10 @@ class ModelsInterface(InvocationContextInterface):
             return self._services.model_manager.store.exists(identifier.key)
 
     def load(
-        self, identifier: Union[str, "ModelIdentifierField"], submodel_type: Optional[SubModelType] = None
+        self,
+        identifier: Union[str, "ModelIdentifierField"],
+        queue_id: str,
+        submodel_type: Optional[SubModelType] = None,
     ) -> LoadedModel:
         """Load a model.
 
@@ -378,18 +371,19 @@ class ModelsInterface(InvocationContextInterface):
 
         if isinstance(identifier, str):
             model = self._services.model_manager.store.get_model(identifier)
+            return self._services.model_manager.load.load_model(model, queue_id, submodel_type)
         else:
-            submodel_type = submodel_type or identifier.submodel_type
+            _submodel_type = submodel_type or identifier.submodel_type
             model = self._services.model_manager.store.get_model(identifier.key)
-
-        message = f"Loading model {model.name}"
-        if submodel_type:
-            message += f" ({submodel_type.value})"
-        self._util.signal_progress(message)
-        return self._services.model_manager.load.load_model(model, submodel_type)
+            return self._services.model_manager.load.load_model(model, queue_id, _submodel_type)
 
     def load_by_attrs(
-        self, name: str, base: BaseModelType, type: ModelType, submodel_type: Optional[SubModelType] = None
+        self,
+        name: str,
+        base: BaseModelType,
+        type: ModelType,
+        queue_id: str,
+        submodel_type: Optional[SubModelType] = None,
     ) -> LoadedModel:
         """Load a model by its attributes.
 
@@ -411,11 +405,7 @@ class ModelsInterface(InvocationContextInterface):
         if len(configs) > 1:
             raise ValueError(f"More than one model found with name {name}, base {base}, and type {type}")
 
-        message = f"Loading model {name}"
-        if submodel_type:
-            message += f" ({submodel_type.value})"
-        self._util.signal_progress(message)
-        return self._services.model_manager.load.load_model(configs[0], submodel_type)
+        return self._services.model_manager.load.load_model(configs[0], queue_id, submodel_type)
 
     def get_config(self, identifier: Union[str, "ModelIdentifierField"]) -> AnyModelConfig:
         """Get a model's config.
@@ -485,12 +475,12 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             Path to the downloaded model
         """
-        self._util.signal_progress(f"Downloading model {source}")
         return self._services.model_manager.install.download_and_cache_model(source=source)
 
     def load_local_model(
         self,
         model_path: Path,
+        queue_id: str,
         loader: Optional[Callable[[Path], AnyModel]] = None,
     ) -> LoadedModelWithoutConfig:
         """
@@ -508,13 +498,14 @@ class ModelsInterface(InvocationContextInterface):
         Returns:
             A LoadedModelWithoutConfig object.
         """
-
-        self._util.signal_progress(f"Loading model {model_path.name}")
-        return self._services.model_manager.load.load_model_from_path(model_path=model_path, loader=loader)
+        return self._services.model_manager.load.load_model_from_path(
+            model_path=model_path, queue_id=queue_id, loader=loader
+        )
 
     def load_remote_model(
         self,
         source: str | AnyHttpUrl,
+        queue_id: str,
         loader: Optional[Callable[[Path], AnyModel]] = None,
     ) -> LoadedModelWithoutConfig:
         """
@@ -535,9 +526,9 @@ class ModelsInterface(InvocationContextInterface):
             A LoadedModelWithoutConfig object.
         """
         model_path = self._services.model_manager.install.download_and_cache_model(source=str(source))
-
-        self._util.signal_progress(f"Loading model {source}")
-        return self._services.model_manager.load.load_model_from_path(model_path=model_path, loader=loader)
+        return self._services.model_manager.load.load_model_from_path(
+            model_path=model_path, queue_id=queue_id, loader=loader
+        )
 
 
 class ConfigInterface(InvocationContextInterface):
@@ -558,6 +549,14 @@ class UtilInterface(InvocationContextInterface):
         super().__init__(services, data)
         self._is_canceled = is_canceled
 
+    def get_queue_id(self) -> str:
+        """Checks if the current session has been canceled.
+
+        Returns:
+            True if the current session has been canceled, False if not.
+        """
+        return self._data.queue_item.queue_id
+
     def is_canceled(self) -> bool:
         """Checks if the current session has been canceled.
 
@@ -730,12 +729,12 @@ def build_invocation_context(
     """
 
     logger = LoggerInterface(services=services, data=data)
+    images = ImagesInterface(services=services, data=data)
     tensors = TensorsInterface(services=services, data=data)
+    models = ModelsInterface(services=services, data=data)
     config = ConfigInterface(services=services, data=data)
     util = UtilInterface(services=services, data=data, is_canceled=is_canceled)
     conditioning = ConditioningInterface(services=services, data=data)
-    models = ModelsInterface(services=services, data=data, util=util)
-    images = ImagesInterface(services=services, data=data, util=util)
     boards = BoardsInterface(services=services, data=data)
 
     ctx = InvocationContext(

invokeai/app/util/ti_utils.py

@@ -22,7 +22,7 @@ def generate_ti_list(
     for trigger in extract_ti_triggers_from_prompt(prompt):
         name_or_key = trigger[1:-1]
         try:
-            loaded_model = context.models.load(name_or_key)
+            loaded_model = context.models.load(name_or_key, queue_id=context.util.get_queue_id())
             model = loaded_model.model
             assert isinstance(model, TextualInversionModelRaw)
             assert loaded_model.config.base == base
@@ -30,7 +30,7 @@ def generate_ti_list(
         except UnknownModelException:
             try:
                 loaded_model = context.models.load_by_attrs(
-                    name=name_or_key, base=base, type=ModelType.TextualInversion
+                    name=name_or_key, base=base, type=ModelType.TextualInversion, queue_id=context.util.get_queue_id()
                 )
                 model = loaded_model.model
                 assert isinstance(model, TextualInversionModelRaw)

invokeai/backend/lora/conversions/flux_diffusers_lora_conversion_utils.py

@@ -45,9 +45,8 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
     # Constants for FLUX.1
     num_double_layers = 19
     num_single_layers = 38
-    hidden_size = 3072
-    mlp_ratio = 4.0
-    mlp_hidden_dim = int(hidden_size * mlp_ratio)
+    # inner_dim = 3072
+    # mlp_ratio = 4.0
 
     layers: dict[str, AnyLoRALayer] = {}
 
@@ -63,43 +62,30 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
             layers[dst_key] = LoRALayer.from_state_dict_values(values=value)
             assert len(src_layer_dict) == 0
 
-    def add_qkv_lora_layer_if_present(
-        src_keys: list[str],
-        src_weight_shapes: list[tuple[int, int]],
-        dst_qkv_key: str,
-        allow_missing_keys: bool = False,
-    ) -> None:
+    def add_qkv_lora_layer_if_present(src_keys: list[str], dst_qkv_key: str) -> None:
         """Handle the Q, K, V matrices for a transformer block. We need special handling because the diffusers format
         stores them in separate matrices, whereas the BFL format used internally by InvokeAI concatenates them.
         """
-        # If none of the keys are present, return early.
+        # We expect that either all src keys are present or none of them are. Verify this.
         keys_present = [key in grouped_state_dict for key in src_keys]
+        assert all(keys_present) or not any(keys_present)
+
+        # If none of the keys are present, return early.
         if not any(keys_present):
             return
 
+        src_layer_dicts = [grouped_state_dict.pop(key) for key in src_keys]
         sub_layers: list[LoRALayer] = []
-        for src_key, src_weight_shape in zip(src_keys, src_weight_shapes, strict=True):
-            src_layer_dict = grouped_state_dict.pop(src_key, None)
-            if src_layer_dict is not None:
-                values = {
-                    "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
-                    "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
-                }
-                if alpha is not None:
-                    values["alpha"] = torch.tensor(alpha)
-                assert values["lora_down.weight"].shape[1] == src_weight_shape[1]
-                assert values["lora_up.weight"].shape[0] == src_weight_shape[0]
-                sub_layers.append(LoRALayer.from_state_dict_values(values=values))
-                assert len(src_layer_dict) == 0
-            else:
-                if not allow_missing_keys:
-                    raise ValueError(f"Missing LoRA layer: '{src_key}'.")
-                values = {
-                    "lora_up.weight": torch.zeros((src_weight_shape[0], 1)),
-                    "lora_down.weight": torch.zeros((1, src_weight_shape[1])),
-                }
-                sub_layers.append(LoRALayer.from_state_dict_values(values=values))
-        layers[dst_qkv_key] = ConcatenatedLoRALayer(lora_layers=sub_layers)
+        for src_layer_dict in src_layer_dicts:
+            values = {
+                "lora_down.weight": src_layer_dict.pop("lora_A.weight"),
+                "lora_up.weight": src_layer_dict.pop("lora_B.weight"),
+            }
+            if alpha is not None:
+                values["alpha"] = torch.tensor(alpha)
+            sub_layers.append(LoRALayer.from_state_dict_values(values=values))
+            assert len(src_layer_dict) == 0
+        layers[dst_qkv_key] = ConcatenatedLoRALayer(lora_layers=sub_layers, concat_axis=0)
 
     # time_text_embed.timestep_embedder -> time_in.
     add_lora_layer_if_present("time_text_embed.timestep_embedder.linear_1", "time_in.in_layer")
@@ -132,7 +118,6 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
                 f"transformer_blocks.{i}.attn.to_k",
                 f"transformer_blocks.{i}.attn.to_v",
             ],
-            [(hidden_size, hidden_size), (hidden_size, hidden_size), (hidden_size, hidden_size)],
             f"double_blocks.{i}.img_attn.qkv",
         )
         add_qkv_lora_layer_if_present(
@@ -141,7 +126,6 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
                 f"transformer_blocks.{i}.attn.add_k_proj",
                 f"transformer_blocks.{i}.attn.add_v_proj",
             ],
-            [(hidden_size, hidden_size), (hidden_size, hidden_size), (hidden_size, hidden_size)],
             f"double_blocks.{i}.txt_attn.qkv",
         )
 
@@ -191,14 +175,7 @@ def lora_model_from_flux_diffusers_state_dict(state_dict: Dict[str, torch.Tensor
                 f"single_transformer_blocks.{i}.attn.to_v",
                 f"single_transformer_blocks.{i}.proj_mlp",
             ],
-            [
-                (hidden_size, hidden_size),
-                (hidden_size, hidden_size),
-                (hidden_size, hidden_size),
-                (mlp_hidden_dim, hidden_size),
-            ],
             f"single_blocks.{i}.linear1",
-            allow_missing_keys=True,
         )
 
         # Output projections.

invokeai/backend/model_manager/config.py

@@ -165,8 +165,6 @@ class SubmodelDefinition(BaseModel):
     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)")

invokeai/backend/model_manager/load/load_default.py

@@ -35,7 +35,6 @@ class ModelLoader(ModelLoaderBase):
         self._logger = logger
         self._ram_cache = ram_cache
         self._torch_dtype = TorchDevice.choose_torch_dtype()
-        self._torch_device = TorchDevice.choose_torch_device()
 
     def load_model(self, model_config: AnyModelConfig, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
         """

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

@@ -84,15 +84,7 @@ class FluxVAELoader(ModelLoader):
             model = AutoEncoder(ae_params[config.config_path])
             sd = load_file(model_path)
             model.load_state_dict(sd, assign=True)
-            # VAE is broken in float16, which mps defaults to
-            if self._torch_dtype == torch.float16:
-                try:
-                    vae_dtype = torch.tensor([1.0], dtype=torch.bfloat16, device=self._torch_device).dtype
-                except TypeError:
-                    vae_dtype = torch.float32
-            else:
-                vae_dtype = self._torch_dtype
-            model.to(vae_dtype)
+            model.to(dtype=self._torch_dtype)
 
         return model
 

invokeai/backend/model_manager/starter_models.py

@@ -300,7 +300,7 @@ ip_adapter_sdxl = StarterModel(
 ip_adapter_flux = StarterModel(
     name="Standard Reference (XLabs FLUX IP-Adapter)",
     base=BaseModelType.Flux,
-    source="https://huggingface.co/XLabs-AI/flux-ip-adapter/resolve/main/ip_adapter.safetensors",
+    source="https://huggingface.co/XLabs-AI/flux-ip-adapter/resolve/main/flux-ip-adapter.safetensors",
     description="References images with a more generalized/looser degree of precision.",
     type=ModelType.IPAdapter,
     dependencies=[clip_vit_l_image_encoder],

invokeai/backend/model_manager/util/model_util.py

@@ -172,8 +172,6 @@ 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:

invokeai/backend/model_manager/util/select_hf_files.py

@@ -85,7 +85,6 @@ 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:
@@ -120,16 +119,10 @@ 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

invokeai/backend/sd3/extensions/inpaint_extension.py

@@ -1,58 +0,0 @@
-import torch
-
-
-class InpaintExtension:
-    """A class for managing inpainting with SD3."""
-
-    def __init__(self, init_latents: torch.Tensor, inpaint_mask: torch.Tensor, noise: torch.Tensor):
-        """Initialize InpaintExtension.
-
-        Args:
-            init_latents (torch.Tensor): The initial latents (i.e. un-noised at timestep 0).
-            inpaint_mask (torch.Tensor): A mask specifying which elements to inpaint. Range [0, 1]. Values of 1 will be
-                re-generated. Values of 0 will remain unchanged. Values between 0 and 1 can be used to blend the
-                inpainted region with the background.
-            noise (torch.Tensor): The noise tensor used to noise the init_latents.
-        """
-        assert init_latents.dim() == inpaint_mask.dim() == noise.dim() == 4
-        assert init_latents.shape[-2:] == inpaint_mask.shape[-2:] == noise.shape[-2:]
-
-        self._init_latents = init_latents
-        self._inpaint_mask = inpaint_mask
-        self._noise = noise
-
-    def _apply_mask_gradient_adjustment(self, t_prev: float) -> torch.Tensor:
-        """Applies inpaint mask gradient adjustment and returns the inpaint mask to be used at the current timestep."""
-        # As we progress through the denoising process, we promote gradient regions of the mask to have a full weight of
-        # 1.0. This helps to produce more coherent seams around the inpainted region. We experimented with a (small)
-        # number of promotion strategies (e.g. gradual promotion based on timestep), but found that a simple cutoff
-        # threshold worked well.
-        # We use a small epsilon to avoid any potential issues with floating point precision.
-        eps = 1e-4
-        mask_gradient_t_cutoff = 0.5
-        if t_prev > mask_gradient_t_cutoff:
-            # Early in the denoising process, use the inpaint mask as-is.
-            return self._inpaint_mask
-        else:
-            # After the cut-off, promote all non-zero mask values to 1.0.
-            mask = self._inpaint_mask.where(self._inpaint_mask <= (0.0 + eps), 1.0)
-
-        return mask
-
-    def merge_intermediate_latents_with_init_latents(
-        self, intermediate_latents: torch.Tensor, t_prev: float
-    ) -> torch.Tensor:
-        """Merge the intermediate latents with the initial latents for the current timestep using the inpaint mask. I.e.
-        update the intermediate latents to keep the regions that are not being inpainted on the correct noise
-        trajectory.
-
-        This function should be called after each denoising step.
-        """
-
-        mask = self._apply_mask_gradient_adjustment(t_prev)
-
-        # Noise the init latents for the current timestep.
-        noised_init_latents = self._noise * t_prev + (1.0 - t_prev) * self._init_latents
-
-        # Merge the intermediate latents with the noised_init_latents using the inpaint_mask.
-        return intermediate_latents * mask + noised_init_latents * (1.0 - mask)

invokeai/backend/stable_diffusion/extensions/lora.py

@@ -29,7 +29,7 @@ class LoRAExt(ExtensionBase):
 
     @contextmanager
     def patch_unet(self, unet: UNet2DConditionModel, original_weights: OriginalWeightsStorage):
-        lora_model = self._node_context.models.load(self._model_id).model
+        lora_model = self._node_context.models.load(self._model_id, self._node_context.util.get_queue_id()).model
         assert isinstance(lora_model, LoRAModelRaw)
         LoRAPatcher.apply_lora_patch(
             model=unet,

invokeai/backend/stable_diffusion/extensions/t2i_adapter.py

@@ -54,7 +54,7 @@ class T2IAdapterExt(ExtensionBase):
     @callback(ExtensionCallbackType.SETUP)
     def setup(self, ctx: DenoiseContext):
         t2i_model: T2IAdapter
-        with self._node_context.models.load(self._model_id) as t2i_model:
+        with self._node_context.models.load(self._model_id, self._node_context.util.get_queue_id()) as t2i_model:
             _, _, latents_height, latents_width = ctx.inputs.orig_latents.shape
 
             self._adapter_state = self._run_model(

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
@@ -319,28 +319,6 @@ 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'}
@@ -1002,6 +980,49 @@ 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:
@@ -4292,10 +4313,6 @@ 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:
@@ -7540,10 +7557,6 @@ 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

@@ -174,8 +174,7 @@
         "placeholderSelectAModel": "Select a model",
         "reset": "Reset",
         "none": "None",
-        "new": "New",
-        "generating": "Generating"
+        "new": "New"
     },
     "hrf": {
         "hrf": "High Resolution Fix",
@@ -705,8 +704,6 @@
         "baseModel": "Base Model",
         "cancel": "Cancel",
         "clipEmbed": "CLIP Embed",
-        "clipLEmbed": "CLIP-L Embed",
-        "clipGEmbed": "CLIP-G Embed",
         "config": "Config",
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
@@ -1000,7 +997,7 @@
         "controlNetControlMode": "Control Mode",
         "copyImage": "Copy Image",
         "denoisingStrength": "Denoising Strength",
-        "disabledNoRasterContent": "Disabled (No Raster Content)",
+        "noRasterLayers": "No Raster Layers",
         "downloadImage": "Download Image",
         "general": "General",
         "guidance": "Guidance",
@@ -1140,7 +1137,6 @@
         "resetWebUI": "Reset Web UI",
         "resetWebUIDesc1": "Resetting the web UI only resets the browser's local cache of your images and remembered settings. It does not delete any images from disk.",
         "resetWebUIDesc2": "If images aren't showing up in the gallery or something else isn't working, please try resetting before submitting an issue on GitHub.",
-        "showDetailedInvocationProgress": "Show Progress Details",
         "showProgressInViewer": "Show Progress Images in Viewer",
         "ui": "User Interface",
         "clearIntermediatesDisabled": "Queue must be empty to clear intermediates",
@@ -1675,7 +1671,7 @@
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
-        "outputOnlyMaskedRegions": "Output Only Generated Regions",
+        "outputOnlyMaskedRegions": "Output Only Masked Regions",
         "addLayer": "Add Layer",
         "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
@@ -1791,7 +1787,7 @@
         },
         "ipAdapterMethod": {
             "ipAdapterMethod": "IP Adapter Method",
-            "full": "Style and Composition",
+            "full": "Full",
             "style": "Style Only",
             "composition": "Composition Only"
         },
@@ -2003,9 +1999,7 @@
         "upscaleModelDesc": "Upscale (image to image) model",
         "missingUpscaleInitialImage": "Missing initial image for upscaling",
         "missingUpscaleModel": "Missing upscale model",
-        "missingTileControlNetModel": "No valid tile ControlNet models installed",
-        "incompatibleBaseModel": "Unsupported main model architecture for upscaling",
-        "incompatibleBaseModelDesc": "Upscaling is supported for SD1.5 and SDXL architecture models only. Change the main model to enable upscaling."
+        "missingTileControlNetModel": "No valid tile ControlNet models installed"
     },
     "stylePresets": {
         "active": "Active",
@@ -2108,10 +2102,8 @@
     },
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
-        "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."
-        ],
+        "line1": "<StrongComponent>Layer Merging</StrongComponent>: New <StrongComponent>Merge Down</StrongComponent> and improved <StrongComponent>Merge Visible</StrongComponent> for all layers, with special handling for Regional Guidance and Control Layers.",
+        "line2": "<StrongComponent>HF Token Support</StrongComponent>: Upload models that require Hugging Face authentication.",
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",
         "watchUiUpdatesOverview": "Watch UI Updates Overview"

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

@@ -8,8 +8,10 @@ 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 {
@@ -17,7 +19,6 @@ 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';
@@ -61,6 +62,8 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
   useGetOpenAPISchemaQuery();
   useSyncLoggingConfig();
 
+  const { dropzone, isHandlingUpload, setIsHandlingUpload } = useFullscreenDropzone();
+
   const handleReset = useCallback(() => {
     clearStorage();
     location.reload();
@@ -89,8 +92,19 @@ 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">
+      <Box
+        id="invoke-app-wrapper"
+        w="100dvw"
+        h="100dvh"
+        position="relative"
+        overflow="hidden"
+        {...dropzone.getRootProps()}
+      >
+        <input {...dropzone.getInputProps()} />
         <AppContent />
+        {dropzone.isDragActive && isHandlingUpload && (
+          <ImageUploadOverlay dropzone={dropzone} setIsHandlingUpload={setIsHandlingUpload} />
+        )}
       </Box>
       <DeleteImageModal />
       <ChangeBoardModal />
@@ -107,7 +121,6 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
       <NewGallerySessionDialog />
       <NewCanvasSessionDialog />
       <ImageContextMenu />
-      <FullscreenDropzone />
     </ErrorBoundary>
   );
 };

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

@@ -1,3 +1,4 @@
+import { skipToken } from '@reduxjs/toolkit/query';
 import { useAppSelector } from 'app/store/storeHooks';
 import { useIsRegionFocused } from 'common/hooks/focus';
 import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
@@ -7,11 +8,13 @@ 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 imageDTO = useAppSelector(selectLastSelectedImage);
+  const lastSelectedImage = useAppSelector(selectLastSelectedImage);
+  const { currentData: imageDTO } = useGetImageDTOQuery(lastSelectedImage?.image_name ?? skipToken);
 
   if (!imageDTO) {
     return null;

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

@@ -19,6 +19,7 @@ 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';
@@ -236,7 +237,9 @@ const InvokeAIUI = ({
       <Provider store={store}>
         <React.Suspense fallback={<Loading />}>
           <ThemeLocaleProvider>
-            <App config={config} studioInitAction={studioInitAction} />
+            <AppDndContext>
+              <App config={config} studioInitAction={studioInitAction} />
+            </AppDndContext>
           </ThemeLocaleProvider>
         </React.Suspense>
       </Provider>

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

@@ -17,7 +17,6 @@ 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

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

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

@@ -16,6 +16,7 @@ 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';
@@ -92,6 +93,9 @@ 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 JsonObject, t('queue.graphQueued'));
+        log.debug({ enqueueResult } as SerializableObject, t('queue.graphQueued'));
       } catch (error) {
-        log.error({ enqueueBatchArg } as JsonObject, t('queue.graphFailedToQueue'));
+        log.error({ enqueueBatchArg } as SerializableObject, 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 JsonObject, 'Batch enqueued');
+      log.debug({ enqueueResult } as SerializableObject, '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 JsonObject, t('queue.batchFailedToQueue'));
+        log.error({ batchConfig } as SerializableObject, t('queue.batchFailedToQueue'));
         return;
       }
 
@@ -71,7 +71,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
           description: t('common.unknownError'),
         });
       }
-      log.error({ batchConfig, error: serializeError(response) } as JsonObject, t('queue.batchFailedToQueue'));
+      log.error({ batchConfig, error: serializeError(response) } as SerializableObject, t('queue.batchFailedToQueue'));
     },
   });
 };

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

@@ -1,22 +1,19 @@
 import { logger } from 'app/logging/logger';
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { extractMessageFromAssertionError } from 'common/util/extractMessageFromAssertionError';
+import type { SerializableObject } from 'common/types';
 import type { Result } from 'common/util/result';
 import { withResult, withResultAsync } from 'common/util/result';
 import { $canvasManager } from 'features/controlLayers/store/ephemeral';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
 import { buildFLUXGraph } from 'features/nodes/util/graph/generation/buildFLUXGraph';
 import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
-import { buildSD3Graph } from 'features/nodes/util/graph/generation/buildSD3Graph';
 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, AssertionError } from 'tsafe';
-import type { JsonObject } from 'type-fest';
+import { assert } from 'tsafe';
 
 const log = logger('generation');
 
@@ -35,8 +32,8 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
       let buildGraphResult: Result<
         {
           g: Graph;
-          noise: Invocation<'noise' | 'flux_denoise' | 'sd3_denoise'>;
-          posCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder' | 'sd3_text_encoder'>;
+          noise: Invocation<'noise' | 'flux_denoise'>;
+          posCond: Invocation<'compel' | 'sdxl_compel_prompt' | 'flux_text_encoder'>;
         },
         Error
       >;
@@ -52,9 +49,6 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
         case `sd-2`:
           buildGraphResult = await withResultAsync(() => buildSD1Graph(state, manager));
           break;
-        case `sd-3`:
-          buildGraphResult = await withResultAsync(() => buildSD3Graph(state, manager));
-          break;
         case `flux`:
           buildGraphResult = await withResultAsync(() => buildFLUXGraph(state, manager));
           break;
@@ -63,17 +57,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
       }
 
       if (buildGraphResult.isErr()) {
-        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,
-        });
+        log.error({ error: serializeError(buildGraphResult.error) }, 'Failed to build graph');
         return;
       }
 
@@ -104,7 +88,7 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
         return;
       }
 
-      log.debug({ batchConfig: prepareBatchResult.value } as JsonObject, 'Enqueued batch');
+      log.debug({ batchConfig: prepareBatchResult.value } as SerializableObject, '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 JsonObject, 'Received OpenAPI schema');
+      log.debug({ schemaJSON: parseify(schemaJSON) } as SerializableObject, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
 
-      log.debug({ nodeTemplates } as JsonObject, `Built ${size(nodeTemplates)} node templates`);
+      log.debug({ nodeTemplates } as SerializableObject, `Built ${size(nodeTemplates)} node templates`);
 
       $templates.set(nodeTemplates);
     },

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

@@ -0,0 +1,333 @@
+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 { 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, initialControlNet } 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 overrides: Partial<CanvasControlLayerState> = {
+          objects: [imageObject],
+          position: { x, y },
+          controlAdapter: deepClone(initialControlNet),
+        };
+        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

@@ -1,8 +1,18 @@
 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';
@@ -41,14 +51,12 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
 
       log.debug({ imageDTO }, 'Image uploaded');
 
-      if (action.meta.arg.originalArgs.silent || imageDTO.is_intermediate) {
-        // When a "silent" upload is requested, or the image is intermediate, we can skip all post-upload actions,
-        // like toasts and switching the gallery view
+      const { postUploadAction } = action.meta.arg.originalArgs;
+
+      if (!postUploadAction) {
         return;
       }
 
-      const boardId = imageDTO.board_id ?? 'none';
-
       const DEFAULT_UPLOADED_TOAST = {
         id: 'IMAGE_UPLOADED',
         title: t('toast.imageUploaded'),
@@ -56,34 +64,80 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
       } as const;
 
       // default action - just upload and alert user
-      if (lastUploadedToastTimeout !== null) {
-        window.clearTimeout(lastUploadedToastTimeout);
+      if (postUploadAction.type === 'TOAST') {
+        const boardId = imageDTO.board_id ?? 'none';
+        if (lastUploadedToastTimeout !== null) {
+          window.clearTimeout(lastUploadedToastTimeout);
+        }
+        const toastApi = toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          title: postUploadAction.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;
       }
-      const toastApi = toast({
-        ...DEFAULT_UPLOADED_TOAST,
-        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'));
+      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;
       }
     },
   });

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

@@ -1,6 +1,7 @@
 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,
@@ -40,7 +41,6 @@ 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<JsonObject>
+  log: Logger<SerializableObject>
 ) => undefined;
 
 const handleMainModels: ModelHandler = (models, state, dispatch, log) => {

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

@@ -3,6 +3,7 @@ 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';
@@ -36,7 +37,6 @@ 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 JsonObject, // this is always serializable
+        diff: diff(parsed, transformed) as SerializableObject, // this is always serializable
       },
       `Rehydrated slice "${key}"`
     );

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

@@ -25,8 +25,7 @@ export type AppFeature =
   | 'invocationCache'
   | 'bulkDownload'
   | 'starterModels'
-  | 'hfToken'
-  | 'invocationProgressAlert';
+  | 'hfToken';
 
 /**
  * A disable-able Stable Diffusion feature

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

@@ -0,0 +1,251 @@
+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/IAIDndImageIcon.tsx

@@ -21,7 +21,7 @@ type Props = Omit<IconButtonProps, 'aria-label' | 'onClick' | 'tooltip'> & {
   tooltip: string;
 };
 
-export const DndImageIcon = memo((props: Props) => {
+const IAIDndImageIcon = (props: Props) => {
   const { onClick, tooltip, icon, ...rest } = props;
 
   return (
@@ -35,6 +35,6 @@ export const DndImageIcon = memo((props: Props) => {
       {...rest}
     />
   );
-});
+};
 
-DndImageIcon.displayName = 'DndImageIcon';
+export default memo(IAIDndImageIcon);

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

@@ -0,0 +1,38 @@
+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

@@ -0,0 +1,64 @@
+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

@@ -0,0 +1,46 @@
+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

@@ -0,0 +1,24 @@
+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 };
 
-const IAILoadingImageFallback = memo((props: Props) => {
+export const IAILoadingImageFallback = memo((props: Props) => {
   if (props.image) {
     return (
       <Skeleton

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

@@ -0,0 +1,28 @@
+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

@@ -0,0 +1,89 @@
+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/OverlayScrollbars/ScrollableContent.tsx

@@ -1,13 +1,9 @@
-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, useEffect, useMemo, useState } from 'react';
+import { memo, useMemo } from 'react';
 
 type Props = PropsWithChildren & {
   maxHeight?: ChakraProps['maxHeight'];
@@ -15,38 +11,17 @@ type Props = PropsWithChildren & {
   overflowY?: 'hidden' | 'scroll';
 };
 
-const styles: CSSProperties = { position: 'absolute', top: 0, left: 0, right: 0, bottom: 0 };
+const styles: CSSProperties = { height: '100%', width: '100%' };
 
 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 ref={osRef} style={styles} options={overlayscrollbarsOptions}>
+        <OverlayScrollbarsComponent defer style={styles} options={overlayscrollbarsOptions}>
           {children}
         </OverlayScrollbarsComponent>
       </Box>

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

@@ -0,0 +1,124 @@
+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

@@ -1,5 +1,3 @@
-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';
@@ -9,23 +7,14 @@ import { useCallback } from 'react';
 import type { FileRejection } from 'react-dropzone';
 import { useDropzone } from 'react-dropzone';
 import { useTranslation } from 'react-i18next';
-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';
+import { useUploadImageMutation } from 'services/api/endpoints/images';
+import type { PostUploadAction } from 'services/api/types';
 
-type UseImageUploadButtonArgs =
-  | {
-      isDisabled?: boolean;
-      allowMultiple: false;
-      onUpload?: (imageDTO: ImageDTO) => void;
-    }
-  | {
-      isDisabled?: boolean;
-      allowMultiple: true;
-      onUpload?: (imageDTOs: ImageDTO[]) => void;
-    };
+type UseImageUploadButtonArgs = {
+  postUploadAction?: PostUploadAction;
+  isDisabled?: boolean;
+  allowMultiple?: boolean;
+};
 
 const log = logger('gallery');
 
@@ -48,53 +37,30 @@ const log = logger('gallery');
  * <Button {...getUploadButtonProps()} /> // will open the file dialog on click
  * <input {...getUploadInputProps()} /> // hidden, handles native upload functionality
  */
-export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: UseImageUploadButtonArgs) => {
+export const useImageUploadButton = ({
+  postUploadAction,
+  isDisabled,
+  allowMultiple = false,
+}: UseImageUploadButtonArgs) => {
   const autoAddBoardId = useAppSelector(selectAutoAddBoardId);
-  const [uploadImage, request] = useUploadImageMutation();
+  const [uploadImage] = useUploadImageMutation();
   const maxImageUploadCount = useAppSelector(selectMaxImageUploadCount);
   const { t } = useTranslation();
 
   const onDropAccepted = useCallback(
-    async (files: File[]) => {
-      if (!allowMultiple) {
-        if (files.length > 1) {
-          log.warn('Multiple files dropped but only one allowed');
-          return;
-        }
-        if (files.length === 0) {
-          // Should never happen
-          log.warn('No files dropped');
-          return;
-        }
-        const file = files[0];
-        assert(file !== undefined); // should never happen
-        const imageDTO = await uploadImage({
+    (files: File[]) => {
+      for (const [i, file] of files.entries()) {
+        uploadImage({
           file,
           image_category: 'user',
           is_intermediate: false,
+          postUploadAction: postUploadAction ?? { type: 'TOAST' },
           board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-          silent: true,
-        }).unwrap();
-        if (onUpload) {
-          onUpload(imageDTO);
-        }
-      } else {
-        const imageDTOs = await uploadImages(
-          files.map((file, i) => ({
-            file,
-            image_category: 'user',
-            is_intermediate: false,
-            board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
-            silent: false,
-            isFirstUploadOfBatch: i === 0,
-          }))
-        );
-        if (onUpload) {
-          onUpload(imageDTOs);
-        }
+          isFirstUploadOfBatch: i === 0,
+        });
       }
     },
-    [allowMultiple, autoAddBoardId, onUpload, uploadImage]
+    [autoAddBoardId, postUploadAction, uploadImage]
   );
 
   const onDropRejected = useCallback(
@@ -137,42 +103,5 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
     maxFiles: maxImageUploadCount,
   });
 
-  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()} />
-    </>
-  );
+  return { getUploadButtonProps, getUploadInputProps, openUploader };
 };

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

@@ -119,20 +119,11 @@ const createSelector = (
             reasons.push({ content: i18n.t('upscaling.exceedsMaxSize') });
           }
         }
-        if (model && !['sd-1', 'sdxl'].includes(model.base)) {
-          // When we are using an upsupported model, do not add the other warnings
-          reasons.push({ content: i18n.t('upscaling.incompatibleBaseModel') });
-        } else {
-          // Using a compatible model, add all warnings
-          if (!model) {
-            reasons.push({ content: i18n.t('parameters.invoke.noModelSelected') });
-          }
-          if (!upscale.upscaleModel) {
-            reasons.push({ content: i18n.t('upscaling.missingUpscaleModel') });
-          }
-          if (!upscale.tileControlnetModel) {
-            reasons.push({ content: i18n.t('upscaling.missingTileControlNetModel') });
-          }
+        if (!upscale.upscaleModel) {
+          reasons.push({ content: i18n.t('upscaling.missingUpscaleModel') });
+        }
+        if (!upscale.tileControlnetModel) {
+          reasons.push({ content: i18n.t('upscaling.missingTileControlNetModel') });
         }
       } else {
         if (canvasIsFiltering) {

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

@@ -0,0 +1,14 @@
+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

@@ -0,0 +1,12 @@
+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

@@ -1,6 +0,0 @@
-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/features/controlLayers/components/CanvasAddEntityButtons.tsx

@@ -9,7 +9,7 @@ import {
   useAddRegionalGuidance,
   useAddRegionalReferenceImage,
 } from 'features/controlLayers/hooks/addLayerHooks';
-import { selectIsFLUX, selectIsSD3 } from 'features/controlLayers/store/paramsSlice';
+import { selectIsFLUX } from 'features/controlLayers/store/paramsSlice';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiPlusBold } from 'react-icons/pi';
@@ -23,7 +23,6 @@ export const CanvasAddEntityButtons = memo(() => {
   const addGlobalReferenceImage = useAddGlobalReferenceImage();
   const addRegionalReferenceImage = useAddRegionalReferenceImage();
   const isFLUX = useAppSelector(selectIsFLUX);
-  const isSD3 = useAppSelector(selectIsSD3);
 
   return (
     <Flex w="full" h="full" justifyContent="center" gap={4}>
@@ -37,7 +36,6 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addGlobalReferenceImage}
-              isDisabled={isSD3}
             >
               {t('controlLayers.globalReferenceImage')}
             </Button>
@@ -63,7 +61,7 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addRegionalGuidance}
-              isDisabled={isFLUX || isSD3}
+              isDisabled={isFLUX}
             >
               {t('controlLayers.regionalGuidance')}
             </Button>
@@ -75,7 +73,7 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addRegionalReferenceImage}
-              isDisabled={isFLUX || isSD3}
+              isDisabled={isFLUX}
             >
               {t('controlLayers.regionalReferenceImage')}
             </Button>
@@ -90,7 +88,6 @@ export const CanvasAddEntityButtons = memo(() => {
               justifyContent="flex-start"
               leftIcon={<PiPlusBold />}
               onClick={addControlLayer}
-              isDisabled={isSD3}
             >
               {t('controlLayers.controlLayer')}
             </Button>

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

@@ -1,45 +0,0 @@
-import { Alert, AlertDescription, AlertIcon, AlertTitle } from '@invoke-ai/ui-library';
-import { useStore } from '@nanostores/react';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
-import { selectSystemShouldShowInvocationProgressDetail } from 'features/system/store/systemSlice';
-import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { $invocationProgressMessage } from 'services/events/stores';
-
-const CanvasAlertsInvocationProgressContent = memo(() => {
-  const { t } = useTranslation();
-  const invocationProgressMessage = useStore($invocationProgressMessage);
-
-  if (!invocationProgressMessage) {
-    return null;
-  }
-
-  return (
-    <Alert status="loading" borderRadius="base" fontSize="sm" shadow="md" w="fit-content">
-      <AlertIcon />
-      <AlertTitle>{t('common.generating')}</AlertTitle>
-      <AlertDescription>{invocationProgressMessage}</AlertDescription>
-    </Alert>
-  );
-});
-CanvasAlertsInvocationProgressContent.displayName = 'CanvasAlertsInvocationProgressContent';
-
-export const CanvasAlertsInvocationProgress = memo(() => {
-  const isProgressMessageAlertEnabled = useFeatureStatus('invocationProgressAlert');
-  const shouldShowInvocationProgressDetail = useAppSelector(selectSystemShouldShowInvocationProgressDetail);
-
-  // The alert is disabled at the system level
-  if (!isProgressMessageAlertEnabled) {
-    return null;
-  }
-
-  // The alert is disabled at the user level
-  if (!shouldShowInvocationProgressDetail) {
-    return null;
-  }
-
-  return <CanvasAlertsInvocationProgressContent />;
-});
-
-CanvasAlertsInvocationProgress.displayName = 'CanvasAlertsInvocationProgress';

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

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

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

@@ -1,59 +0,0 @@
-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

@@ -1,181 +0,0 @@
-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/EntityListGlobalActionBarAddLayerMenu.tsx

@@ -9,7 +9,7 @@ import {
   useAddRegionalReferenceImage,
 } from 'features/controlLayers/hooks/addLayerHooks';
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
-import { selectIsFLUX, selectIsSD3 } from 'features/controlLayers/store/paramsSlice';
+import { selectIsFLUX } from 'features/controlLayers/store/paramsSlice';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiPlusBold } from 'react-icons/pi';
@@ -24,7 +24,6 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
   const addRasterLayer = useAddRasterLayer();
   const addControlLayer = useAddControlLayer();
   const isFLUX = useAppSelector(selectIsFLUX);
-  const isSD3 = useAppSelector(selectIsSD3);
 
   return (
     <Menu>
@@ -41,7 +40,7 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
       />
       <MenuList>
         <MenuGroup title={t('controlLayers.global')}>
-          <MenuItem icon={<PiPlusBold />} onClick={addGlobalReferenceImage} isDisabled={isSD3}>
+          <MenuItem icon={<PiPlusBold />} onClick={addGlobalReferenceImage}>
             {t('controlLayers.globalReferenceImage')}
           </MenuItem>
         </MenuGroup>
@@ -49,15 +48,15 @@ export const EntityListGlobalActionBarAddLayerMenu = memo(() => {
           <MenuItem icon={<PiPlusBold />} onClick={addInpaintMask}>
             {t('controlLayers.inpaintMask')}
           </MenuItem>
-          <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance} isDisabled={isFLUX || isSD3}>
+          <MenuItem icon={<PiPlusBold />} onClick={addRegionalGuidance} isDisabled={isFLUX}>
             {t('controlLayers.regionalGuidance')}
           </MenuItem>
-          <MenuItem icon={<PiPlusBold />} onClick={addRegionalReferenceImage} isDisabled={isFLUX || isSD3}>
+          <MenuItem icon={<PiPlusBold />} onClick={addRegionalReferenceImage} isDisabled={isFLUX}>
             {t('controlLayers.regionalReferenceImage')}
           </MenuItem>
         </MenuGroup>
         <MenuGroup title={t('controlLayers.layer_other')}>
-          <MenuItem icon={<PiPlusBold />} onClick={addControlLayer} isDisabled={isSD3}>
+          <MenuItem icon={<PiPlusBold />} onClick={addControlLayer}>
             {t('controlLayers.controlLayer')}
           </MenuItem>
           <MenuItem icon={<PiPlusBold />} onClick={addRasterLayer}>

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

@@ -1,85 +0,0 @@
-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 { firefoxDndFix } from 'features/dnd/util';
-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(
-      firefoxDndFix(element),
-      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/CanvasMainPanelContent.tsx

@@ -21,8 +21,6 @@ import { GatedImageViewer } from 'features/gallery/components/ImageViewer/ImageV
 import { memo, useCallback, useRef } from 'react';
 import { PiDotsThreeOutlineVerticalFill } from 'react-icons/pi';
 
-import { CanvasAlertsInvocationProgress } from './CanvasAlerts/CanvasAlertsInvocationProgress';
-
 const MenuContent = () => {
   return (
     <CanvasManagerProviderGate>
@@ -86,7 +84,6 @@ export const CanvasMainPanelContent = memo(() => {
                 <CanvasAlertsSelectedEntityStatus />
                 <CanvasAlertsPreserveMask />
                 <CanvasAlertsSendingToGallery />
-                <CanvasAlertsInvocationProgress />
               </Flex>
               <Flex position="absolute" top={1} insetInlineEnd={1}>
                 <Menu>
@@ -112,9 +109,7 @@ export const CanvasMainPanelContent = memo(() => {
           <SelectObject />
         </CanvasManagerProviderGate>
       </Flex>
-      <CanvasManagerProviderGate>
-        <CanvasDropArea />
-      </CanvasManagerProviderGate>
+      <CanvasDropArea />
       <GatedImageViewer />
     </Flex>
   );

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

@@ -1,20 +1,16 @@
-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 { useDndContext } from '@dnd-kit/core';
 import { Box, Button, Spacer, Tab, TabList, TabPanel, TabPanels, Tabs } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector, useAppStore } from 'app/store/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import IAIDropOverlay from 'common/components/IAIDropOverlay';
 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, useEffect, useMemo, useRef, useState } from 'react';
+import { memo, useCallback, useMemo, useRef, useState } from 'react';
 import { useTranslation } from 'react-i18next';
 
 export const CanvasRightPanel = memo(() => {
@@ -83,13 +79,37 @@ CanvasRightPanel.displayName = 'CanvasRightPanel';
 
 const PanelTabs = memo(() => {
   const { t } = useTranslation();
-  const store = useAppStore();
+  const activeTab = useAppSelector(selectActiveTabCanvasRightPanel);
   const activeEntityCount = useAppSelector(selectEntityCountActive);
-  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 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 layersTabLabel = useMemo(() => {
     if (activeEntityCount === 0) {
@@ -98,172 +118,23 @@ 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 ref={layersTabRef} position="relative" w={32}>
+      <Tab position="relative" onMouseOver={onOnMouseOverLayersTab} onMouseOut={onMouseOut} w={32}>
         <Box as="span" w="full">
           {layersTabLabel}
         </Box>
-        <DndDropOverlay dndState={layersTabDndState} withBackdrop={false} />
+        {dndCtx.active && activeTab !== 'layers' && (
+          <IAIDropOverlay isOver={mouseOverTab === 'layers'} withBackdrop={false} />
+        )}
       </Tab>
-      <Tab ref={galleryTabRef} position="relative" w={32}>
+      <Tab position="relative" onMouseOver={onOnMouseOverGalleryTab} onMouseOut={onMouseOut} w={32}>
         <Box as="span" w="full">
           {t('gallery.gallery')}
         </Box>
-        <DndDropOverlay dndState={galleryTabDndState} withBackdrop={false} />
+        {dndCtx.active && activeTab !== 'gallery' && (
+          <IAIDropOverlay isOver={mouseOverTab === 'gallery'} withBackdrop={false} />
+        )}
       </Tab>
     </>
   );

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

@@ -1,5 +1,6 @@
 import { Spacer } from '@invoke-ai/ui-library';
-import { CanvasEntityContainer } from 'features/controlLayers/components/CanvasEntityList/CanvasEntityContainer';
+import IAIDroppable from 'common/components/IAIDroppable';
+import { CanvasEntityContainer } from 'features/controlLayers/components/common/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';
@@ -9,11 +10,8 @@ import { ControlLayerBadges } from 'features/controlLayers/components/ControlLay
 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 { ReplaceCanvasEntityObjectsWithImageDndTargetData } from 'features/dnd/dnd';
-import { replaceCanvasEntityObjectsWithImageDndTarget } from 'features/dnd/dnd';
-import { DndDropTarget } from 'features/dnd/DndDropTarget';
+import type { ReplaceLayerImageDropData } from 'features/dnd/types';
 import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 
@@ -23,16 +21,14 @@ 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 dndTargetData = useMemo<ReplaceCanvasEntityObjectsWithImageDndTargetData>(
-    () => replaceCanvasEntityObjectsWithImageDndTarget.getData({ entityIdentifier }, entityIdentifier.id),
-    [entityIdentifier]
+  const dropData = useMemo<ReplaceLayerImageDropData>(
+    () => ({ id, actionType: 'REPLACE_LAYER_WITH_IMAGE', context: { entityIdentifier } }),
+    [id, entityIdentifier]
   );
-
   return (
     <EntityIdentifierContext.Provider value={entityIdentifier}>
       <ControlLayerAdapterGate>
@@ -47,12 +43,7 @@ export const ControlLayer = memo(({ id }: Props) => {
           <CanvasEntitySettingsWrapper>
             <ControlLayerSettings />
           </CanvasEntitySettingsWrapper>
-          <DndDropTarget
-            dndTarget={replaceCanvasEntityObjectsWithImageDndTarget}
-            dndTargetData={dndTargetData}
-            label={t('controlLayers.replaceLayer')}
-            isDisabled={isBusy}
-          />
+          <IAIDroppable data={dropData} dropLabel={t('controlLayers.replaceLayer')} />
         </CanvasEntityContainer>
       </ControlLayerAdapterGate>
     </EntityIdentifierContext.Provider>

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

@@ -1,7 +1,6 @@
 import { Flex, IconButton } from '@invoke-ai/ui-library';
 import { createMemoizedAppSelector } from 'app/store/createMemoizedSelector';
-import { useAppStore } from 'app/store/nanostores/store';
-import { useAppSelector } from 'app/store/storeHooks';
+import { useAppDispatch, 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';
@@ -22,11 +21,10 @@ 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, ImageDTO, T2IAdapterModelConfig } from 'services/api/types';
+import type { ControlNetModelConfig, PostUploadAction, T2IAdapterModelConfig } from 'services/api/types';
 
 const useControlLayerControlAdapter = (entityIdentifier: CanvasEntityIdentifier<'control_layer'>) => {
   const selectControlAdapter = useMemo(
@@ -43,7 +41,7 @@ const useControlLayerControlAdapter = (entityIdentifier: CanvasEntityIdentifier<
 
 export const ControlLayerControlAdapter = memo(() => {
   const { t } = useTranslation();
-  const { dispatch, getState } = useAppStore();
+  const dispatch = useAppDispatch();
   const entityIdentifier = useEntityIdentifierContext('control_layer');
   const controlAdapter = useControlLayerControlAdapter(entityIdentifier);
   const filter = useEntityFilter(entityIdentifier);
@@ -115,17 +113,11 @@ export const ControlLayerControlAdapter = memo(() => {
 
   const pullBboxIntoLayer = usePullBboxIntoLayer(entityIdentifier);
   const isBusy = useCanvasIsBusy();
-  const uploadOptions = useMemo(
-    () =>
-      ({
-        onUpload: (imageDTO: ImageDTO) => {
-          replaceCanvasEntityObjectsWithImage({ entityIdentifier, imageDTO, dispatch, getState });
-        },
-        allowMultiple: false,
-      }) as const,
-    [dispatch, entityIdentifier, getState]
+  const postUploadAction = useMemo<PostUploadAction>(
+    () => ({ type: 'REPLACE_LAYER_WITH_IMAGE', entityIdentifier }),
+    [entityIdentifier]
   );
-  const uploadApi = useImageUploadButton(uploadOptions);
+  const uploadApi = useImageUploadButton({ postUploadAction });
 
   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/CanvasEntityList/CanvasEntityGroupList';
+import { CanvasEntityGroupList } from 'features/controlLayers/components/common/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 selectEntityIdentifiers = createMemoizedSelector(selectCanvasSlice, (canvas) => {
-  return canvas.controlLayers.entities.map(getEntityIdentifier).toReversed();
+const selectEntityIds = createMemoizedSelector(selectCanvasSlice, (canvas) => {
+  return canvas.controlLayers.entities.map(mapId).reverse();
 });
 
 const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selectedEntityIdentifier) => {
@@ -17,17 +17,17 @@ const selectIsSelected = createSelector(selectSelectedEntityIdentifier, (selecte
 
 export const ControlLayerEntityList = memo(() => {
   const isSelected = useAppSelector(selectIsSelected);
-  const entityIdentifiers = useAppSelector(selectEntityIdentifiers);
+  const layerIds = useAppSelector(selectEntityIds);
 
-  if (entityIdentifiers.length === 0) {
+  if (layerIds.length === 0) {
     return null;
   }
 
-  if (entityIdentifiers.length > 0) {
+  if (layerIds.length > 0) {
     return (
-      <CanvasEntityGroupList type="control_layer" isSelected={isSelected} entityIdentifiers={entityIdentifiers}>
-        {entityIdentifiers.map((entityIdentifier) => (
-          <ControlLayer key={entityIdentifier.id} id={entityIdentifier.id} />
+      <CanvasEntityGroupList type="control_layer" isSelected={isSelected}>
+        {layerIds.map((id) => (
+          <ControlLayer key={id} id={id} />
         ))}
       </CanvasEntityGroupList>
     );

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

@@ -1,25 +1,22 @@
 import { Button, Flex, Text } from '@invoke-ai/ui-library';
-import { useAppStore } from 'app/store/nanostores/store';
+import { useAppDispatch } from 'app/store/storeHooks';
 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 { memo, useCallback, useMemo } from 'react';
 import { Trans } from 'react-i18next';
-import type { ImageDTO } from 'services/api/types';
+import type { PostUploadAction } from 'services/api/types';
 
 export const ControlLayerSettingsEmptyState = memo(() => {
   const entityIdentifier = useEntityIdentifierContext('control_layer');
-  const { dispatch, getState } = useAppStore();
+  const dispatch = useAppDispatch();
   const isBusy = useCanvasIsBusy();
-  const onUpload = useCallback(
-    (imageDTO: ImageDTO) => {
-      replaceCanvasEntityObjectsWithImage({ imageDTO, entityIdentifier, dispatch, getState });
-    },
-    [dispatch, entityIdentifier, getState]
+  const postUploadAction = useMemo<PostUploadAction>(
+    () => ({ type: 'REPLACE_LAYER_WITH_IMAGE', entityIdentifier }),
+    [entityIdentifier]
   );
-  const uploadApi = useImageUploadButton({ onUpload, allowMultiple: false });
+  const uploadApi = useImageUploadButton({ postUploadAction });
   const onClickGalleryButton = useCallback(() => {
     dispatch(activeTabCanvasRightPanelChanged('gallery'));
   }, [dispatch]);

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/CanvasEntityList/CanvasEntityContainer';
+import { CanvasEntityContainer } from 'features/controlLayers/components/common/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

@@ -1,80 +1,82 @@
 import { Flex } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { skipToken } from '@reduxjs/toolkit/query';
-import { UploadImageButton } from 'common/hooks/useImageUploadButton';
+import IAIDndImage from 'common/components/IAIDndImage';
+import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
+import { useNanoid } from 'common/hooks/useNanoid';
 import type { ImageWithDims } from 'features/controlLayers/store/types';
-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 type { ImageDraggableData, TypesafeDroppableData } from 'features/dnd/types';
+import { memo, useCallback, useEffect, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowCounterClockwiseBold } from 'react-icons/pi';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
-import type { ImageDTO } from 'services/api/types';
+import type { ImageDTO, PostUploadAction } from 'services/api/types';
 import { $isConnected } from 'services/events/stores';
 
-type Props<T extends typeof setGlobalReferenceImageDndTarget | typeof setRegionalGuidanceReferenceImageDndTarget> = {
+type Props = {
   image: ImageWithDims | null;
   onChangeImage: (imageDTO: ImageDTO | null) => void;
-  dndTarget: T;
-  dndTargetData: ReturnType<T['getData']>;
+  droppableData: TypesafeDroppableData;
+  postUploadAction: PostUploadAction;
 };
 
-export const IPAdapterImagePreview = memo(
-  <T extends typeof setGlobalReferenceImageDndTarget | typeof setRegionalGuidanceReferenceImageDndTarget>({
-    image,
-    onChangeImage,
-    dndTarget,
-    dndTargetData,
-  }: Props<T>) => {
-    const { t } = useTranslation();
-    const isConnected = useStore($isConnected);
-    const { currentData: imageDTO, isError } = useGetImageDTOQuery(image?.image_name ?? skipToken);
-    const handleResetControlImage = useCallback(() => {
-      onChangeImage(null);
-    }, [onChangeImage]);
+export const IPAdapterImagePreview = memo(({ image, onChangeImage, droppableData, postUploadAction }: Props) => {
+  const { t } = useTranslation();
+  const isConnected = useStore($isConnected);
+  const dndId = useNanoid('ip_adapter_image_preview');
 
-    useEffect(() => {
-      if (isConnected && isError) {
-        handleResetControlImage();
-      }
-    }, [handleResetControlImage, isError, isConnected]);
+  const { currentData: controlImage, isError: isErrorControlImage } = useGetImageDTOQuery(
+    image?.image_name ?? skipToken
+  );
+  const handleResetControlImage = useCallback(() => {
+    onChangeImage(null);
+  }, [onChangeImage]);
 
-    const onUpload = useCallback(
-      (imageDTO: ImageDTO) => {
-        onChangeImage(imageDTO);
-      },
-      [onChangeImage]
-    );
+  const draggableData = useMemo<ImageDraggableData | undefined>(() => {
+    if (controlImage) {
+      return {
+        id: dndId,
+        payloadType: 'IMAGE_DTO',
+        payload: { imageDTO: controlImage },
+      };
+    }
+  }, [controlImage, dndId]);
 
-    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}
+  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')}
           />
-        )}
-        {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>
-    );
-  }
-);
+        </Flex>
+      )}
+    </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/CanvasEntityList/CanvasEntityGroupList';
+import { CanvasEntityGroupList } from 'features/controlLayers/components/common/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 selectEntityIdentifiers = createMemoizedSelector(selectCanvasSlice, (canvas) => {
-  return canvas.referenceImages.entities.map(getEntityIdentifier).toReversed();
+const selectEntityIds = createMemoizedSelector(selectCanvasSlice, (canvas) => {
+  return canvas.referenceImages.entities.map(mapId).reverse();
 });
 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 entityIdentifiers = useAppSelector(selectEntityIdentifiers);
+  const ipaIds = useAppSelector(selectEntityIds);
 
-  if (entityIdentifiers.length === 0) {
+  if (ipaIds.length === 0) {
     return null;
   }
 
-  if (entityIdentifiers.length > 0) {
+  if (ipaIds.length > 0) {
     return (
-      <CanvasEntityGroupList type="reference_image" isSelected={isSelected} entityIdentifiers={entityIdentifiers}>
-        {entityIdentifiers.map((entityIdentifiers) => (
-          <IPAdapter key={entityIdentifiers.id} id={entityIdentifiers.id} />
+      <CanvasEntityGroupList type="reference_image" isSelected={isSelected}>
+        {ipaIds.map((id) => (
+          <IPAdapter key={id} id={id} />
         ))}
       </CanvasEntityGroupList>
     );