Restore line that was accidentally removed during development.

## Summary

This PR contains several improvements to memory management for FLUX
workflows.

It is now possible to achieve better FLUX model caching performance, but
this still requires users to manually configure their `ram`/`vram`
settings. E.g. a `vram` setting of 16.0 should allow for all quantized
FLUX models to be kept in memory on the GPU.

Changes:
- Check the size of a model on disk and free the requisite space in the
model cache before loading it. (This behaviour existed previously, but
was removed in https://github.com/invoke-ai/InvokeAI/pull/6072/files.
The removal did not seem to be intentional).
- Removed the hack to free 24GB of space in the cache before loading the
FLUX model.
- Split the T5 embedding and CLIP embedding steps into separate
functions so that the two models don't both have to be held in RAM at
the same time.
- Fix a bug in `InvokeLinear8bitLt` that was causing some tensors to be
left on the GPU when the model was offloaded to the CPU. (This class is
getting very messy due to the non-standard state_dict handling in
`bnb.nn.Linear8bitLt`. )
- Tidy up some dtype handling in FluxTextToImageInvocation to avoid
situations where we hold references to two copies of the same tensor
unnecessarily.
- (minor) Misc cleanup of ModelCache: improve docs and remove unused
vars.

Future:
We should revisit our default ram/vram configs. The current defaults are
very conservative, and users could see major performance improvements
from tuning these values.

## QA Instructions

I tested the FLUX workflow with the following configurations and
verified that the cache hit rates and memory usage matched the expected
behaviour:
- `ram = 16` and `vram = 16`
- `ram = 16` and `vram = 1`
- `ram = 1` and `vram = 1`

Note that the changes in this PR are not isolated to FLUX. Since we now
check the size of models on disk, we may see slight changes in model
cache offload patterns for other models as well.

## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_

invokeai/app/api/routers/session_queue.py

@@ -11,7 +11,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByOriginResult,
     ClearResult,
     EnqueueBatchResult,
     PruneResult,
@@ -106,19 +105,6 @@ async def cancel_by_batch_ids(
     return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
 
 
-@session_queue_router.put(
-    "/{queue_id}/cancel_by_origin",
-    operation_id="cancel_by_origin",
-    responses={200: {"model": CancelByBatchIDsResult}},
-)
-async def cancel_by_origin(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    origin: str = Query(description="The origin to cancel all queue items for"),
-) -> CancelByOriginResult:
-    """Immediately cancels all queue items with the given origin"""
-    return ApiDependencies.invoker.services.session_queue.cancel_by_origin(queue_id=queue_id, origin=origin)
-
-
 @session_queue_router.put(
     "/{queue_id}/clear",
     operation_id="clear",

invokeai/app/invocations/denoise_latents.py

@@ -185,7 +185,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     )
     denoise_mask: Optional[DenoiseMaskField] = InputField(
         default=None,
-        description=FieldDescriptions.mask,
+        description=FieldDescriptions.denoise_mask,
         input=Input.Connection,
         ui_order=8,
     )

invokeai/app/invocations/fields.py

@@ -45,11 +45,13 @@ class UIType(str, Enum, metaclass=MetaEnum):
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
     VAEModel = "VAEModelField"
+    FluxVAEModel = "FluxVAEModelField"
     LoRAModel = "LoRAModelField"
     ControlNetModel = "ControlNetModelField"
     IPAdapterModel = "IPAdapterModelField"
     T2IAdapterModel = "T2IAdapterModelField"
     T5EncoderModel = "T5EncoderModelField"
+    CLIPEmbedModel = "CLIPEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -128,6 +130,7 @@ class FieldDescriptions:
     noise = "Noise tensor"
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
     t5_encoder = "T5 tokenizer and text encoder"
+    clip_embed_model = "CLIP Embed loader"
     unet = "UNet (scheduler, LoRAs)"
     transformer = "Transformer"
     vae = "VAE"
@@ -178,7 +181,7 @@ class FieldDescriptions:
     )
     num_1 = "The first number"
     num_2 = "The second number"
-    mask = "The mask to use for the operation"
+    denoise_mask = "A mask of the region to apply the denoising process to."
     board = "The board to save the image to"
     image = "The image to process"
     tile_size = "Tile size"

invokeai/app/invocations/flux_denoise.py

@@ -0,0 +1,274 @@
+from typing import Callable, Optional
+
+import torch
+import torchvision.transforms as tv_transforms
+from torchvision.transforms.functional import resize as tv_resize
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import (
+    DenoiseMaskField,
+    FieldDescriptions,
+    FluxConditioningField,
+    Input,
+    InputField,
+    LatentsField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import TransformerField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.session_processor.session_processor_common import CanceledException
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.denoise import denoise
+from invokeai.backend.flux.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.model import Flux
+from invokeai.backend.flux.sampling_utils import (
+    clip_timestep_schedule,
+    generate_img_ids,
+    get_noise,
+    get_schedule,
+    pack,
+    unpack,
+)
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "flux_denoise",
+    title="FLUX Denoise",
+    tags=["image", "flux"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Run denoising process with a FLUX transformer model."""
+
+    # If latents is provided, this means we are doing image-to-image.
+    latents: Optional[LatentsField] = InputField(
+        default=None,
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    # denoise_mask is used for image-to-image inpainting. Only the masked region is modified.
+    denoise_mask: Optional[DenoiseMaskField] = InputField(
+        default=None,
+        description=FieldDescriptions.denoise_mask,
+        input=Input.Connection,
+    )
+    denoising_start: float = InputField(
+        default=0.0,
+        ge=0,
+        le=1,
+        description=FieldDescriptions.denoising_start,
+    )
+    denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
+    transformer: TransformerField = InputField(
+        description=FieldDescriptions.flux_model,
+        input=Input.Connection,
+        title="Transformer",
+    )
+    positive_text_conditioning: FluxConditioningField = InputField(
+        description=FieldDescriptions.positive_cond, input=Input.Connection
+    )
+    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
+    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
+    num_steps: int = InputField(
+        default=4, description="Number of diffusion steps. Recommended values are schnell: 4, dev: 50."
+    )
+    guidance: float = InputField(
+        default=4.0,
+        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
+    )
+    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        latents = self._run_diffusion(context)
+        latents = latents.detach().to("cpu")
+
+        name = context.tensors.save(tensor=latents)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
+
+    def _run_diffusion(
+        self,
+        context: InvocationContext,
+    ):
+        inference_dtype = torch.bfloat16
+
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        flux_conditioning = cond_data.conditionings[0]
+        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+        flux_conditioning = flux_conditioning.to(dtype=inference_dtype)
+        t5_embeddings = flux_conditioning.t5_embeds
+        clip_embeddings = flux_conditioning.clip_embeds
+
+        # Load the input latents, if provided.
+        init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
+        if init_latents is not None:
+            init_latents = init_latents.to(device=TorchDevice.choose_torch_device(), dtype=inference_dtype)
+
+        # Prepare input noise.
+        noise = get_noise(
+            num_samples=1,
+            height=self.height,
+            width=self.width,
+            device=TorchDevice.choose_torch_device(),
+            dtype=inference_dtype,
+            seed=self.seed,
+        )
+
+        transformer_info = context.models.load(self.transformer.transformer)
+        is_schnell = "schnell" in transformer_info.config.config_path
+
+        # Calculate the timestep schedule.
+        image_seq_len = noise.shape[-1] * noise.shape[-2] // 4
+        timesteps = get_schedule(
+            num_steps=self.num_steps,
+            image_seq_len=image_seq_len,
+            shift=not is_schnell,
+        )
+
+        # Clip the timesteps schedule based on denoising_start and denoising_end.
+        timesteps = clip_timestep_schedule(timesteps, self.denoising_start, self.denoising_end)
+
+        # Prepare input latent image.
+        if init_latents is not None:
+            # If init_latents is provided, we are doing image-to-image.
+
+            if is_schnell:
+                context.logger.warning(
+                    "Running image-to-image with a FLUX schnell model. This is not recommended. The results are likely "
+                    "to be poor. Consider using a FLUX dev model instead."
+                )
+
+            # Noise the orig_latents by the appropriate amount for the first timestep.
+            t_0 = timesteps[0]
+            x = t_0 * noise + (1.0 - t_0) * init_latents
+        else:
+            # init_latents are not provided, so we are not doing image-to-image (i.e. we are starting from pure noise).
+            if self.denoising_start > 1e-5:
+                raise ValueError("denoising_start should be 0 when initial latents are not provided.")
+
+            x = noise
+
+        # If len(timesteps) == 1, then short-circuit. We are just noising the input latents, but not taking any
+        # denoising steps.
+        if len(timesteps) <= 1:
+            return x
+
+        inpaint_mask = self._prep_inpaint_mask(context, x)
+
+        b, _c, h, w = x.shape
+        img_ids = generate_img_ids(h=h, w=w, batch_size=b, device=x.device, dtype=x.dtype)
+
+        bs, t5_seq_len, _ = t5_embeddings.shape
+        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
+
+        # Pack all latent tensors.
+        init_latents = pack(init_latents) if init_latents is not None else None
+        inpaint_mask = pack(inpaint_mask) if inpaint_mask is not None else None
+        noise = pack(noise)
+        x = pack(x)
+
+        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
+        assert image_seq_len == x.shape[1]
+
+        # Prepare inpaint extension.
+        inpaint_extension: InpaintExtension | None = None
+        if inpaint_mask is not None:
+            assert init_latents is not None
+            inpaint_extension = InpaintExtension(
+                init_latents=init_latents,
+                inpaint_mask=inpaint_mask,
+                noise=noise,
+            )
+
+        with transformer_info as transformer:
+            assert isinstance(transformer, Flux)
+
+            x = denoise(
+                model=transformer,
+                img=x,
+                img_ids=img_ids,
+                txt=t5_embeddings,
+                txt_ids=txt_ids,
+                vec=clip_embeddings,
+                timesteps=timesteps,
+                step_callback=self._build_step_callback(context),
+                guidance=self.guidance,
+                inpaint_extension=inpaint_extension,
+            )
+
+        x = unpack(x.float(), self.height, self.width)
+        return x
+
+    def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor) -> torch.Tensor | None:
+        """Prepare the inpaint mask.
+
+        - Loads the mask
+        - Resizes if necessary
+        - Casts to same device/dtype as latents
+        - Expands mask to the same shape as latents so that they line up after 'packing'
+
+        Args:
+            context (InvocationContext): The invocation context, for loading the inpaint mask.
+            latents (torch.Tensor): A latent image tensor. In 'unpacked' format. Used to determine the target shape,
+                device, and dtype for the inpaint mask.
+
+        Returns:
+            torch.Tensor | None: Inpaint mask.
+        """
+        if self.denoise_mask is None:
+            return None
+
+        mask = context.tensors.load(self.denoise_mask.mask_name)
+
+        _, _, latent_height, latent_width = latents.shape
+        mask = tv_resize(
+            img=mask,
+            size=[latent_height, latent_width],
+            interpolation=tv_transforms.InterpolationMode.BILINEAR,
+            antialias=False,
+        )
+
+        mask = mask.to(device=latents.device, dtype=latents.dtype)
+
+        # Expand the inpaint mask to the same shape as `latents` so that when we 'pack' `mask` it lines up with
+        # `latents`.
+        return mask.expand_as(latents)
+
+    def _build_step_callback(self, context: InvocationContext) -> Callable[[], None]:
+        def step_callback() -> None:
+            if context.util.is_canceled():
+                raise CanceledException
+
+            # TODO: Make this look like the image before re-enabling
+            # latent_image = unpack(img.float(), self.height, self.width)
+            # latent_image = latent_image.squeeze()  # Remove unnecessary dimensions
+            # flattened_tensor = latent_image.reshape(-1)  # Flatten to shape [48*128*128]
+
+            # # Create a new tensor of the required shape [255, 255, 3]
+            # latent_image = flattened_tensor[: 255 * 255 * 3].reshape(255, 255, 3)  # Reshape to RGB format
+
+            # # Convert to a NumPy array and then to a PIL Image
+            # image = Image.fromarray(latent_image.cpu().numpy().astype(np.uint8))
+
+            # (width, height) = image.size
+            # width *= 8
+            # height *= 8
+
+            # dataURL = image_to_dataURL(image, image_format="JPEG")
+
+            # # TODO: move this whole function to invocation context to properly reference these variables
+            # context._services.events.emit_invocation_denoise_progress(
+            #     context._data.queue_item,
+            #     context._data.invocation,
+            #     state,
+            #     ProgressImage(dataURL=dataURL, width=width, height=height),
+            # )
+
+        return step_callback

invokeai/app/invocations/flux_text_encoder.py

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

invokeai/app/invocations/flux_text_to_image.py

@@ -1,172 +0,0 @@
-import torch
-from einops import rearrange
-from PIL import Image
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    FluxConditioningField,
-    Input,
-    InputField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import TransformerField, VAEField
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.session_processor.session_processor_common import CanceledException
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
-from invokeai.backend.flux.sampling import denoise, get_noise, get_schedule, prepare_latent_img_patches, unpack
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
-from invokeai.backend.util.devices import TorchDevice
-
-
-@invocation(
-    "flux_text_to_image",
-    title="FLUX Text to Image",
-    tags=["image", "flux"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Text-to-image generation using a FLUX model."""
-
-    transformer: TransformerField = InputField(
-        description=FieldDescriptions.flux_model,
-        input=Input.Connection,
-        title="Transformer",
-    )
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-    positive_text_conditioning: FluxConditioningField = InputField(
-        description=FieldDescriptions.positive_cond, input=Input.Connection
-    )
-    width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
-    height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
-    num_steps: int = InputField(
-        default=4, description="Number of diffusion steps. Recommend values are schnell: 4, dev: 50."
-    )
-    guidance: float = InputField(
-        default=4.0,
-        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
-    )
-    seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        flux_conditioning = cond_data.conditionings[0]
-        assert isinstance(flux_conditioning, FLUXConditioningInfo)
-
-        latents = self._run_diffusion(context, flux_conditioning.clip_embeds, flux_conditioning.t5_embeds)
-        image = self._run_vae_decoding(context, latents)
-        image_dto = context.images.save(image=image)
-        return ImageOutput.build(image_dto)
-
-    def _run_diffusion(
-        self,
-        context: InvocationContext,
-        clip_embeddings: torch.Tensor,
-        t5_embeddings: torch.Tensor,
-    ):
-        transformer_info = context.models.load(self.transformer.transformer)
-        inference_dtype = torch.bfloat16
-
-        # Prepare input noise.
-        x = get_noise(
-            num_samples=1,
-            height=self.height,
-            width=self.width,
-            device=TorchDevice.choose_torch_device(),
-            dtype=inference_dtype,
-            seed=self.seed,
-        )
-
-        img, img_ids = prepare_latent_img_patches(x)
-
-        is_schnell = "schnell" in transformer_info.config.config_path
-
-        timesteps = get_schedule(
-            num_steps=self.num_steps,
-            image_seq_len=img.shape[1],
-            shift=not is_schnell,
-        )
-
-        bs, t5_seq_len, _ = t5_embeddings.shape
-        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
-
-        # HACK(ryand): Manually empty the cache. Currently we don't check the size of the model before loading it from
-        # disk. Since the transformer model is large (24GB), there's a good chance that it will OOM on 32GB RAM systems
-        # if the cache is not empty.
-        context.models._services.model_manager.load.ram_cache.make_room(24 * 2**30)
-
-        with transformer_info as transformer:
-            assert isinstance(transformer, Flux)
-
-            def step_callback() -> None:
-                if context.util.is_canceled():
-                    raise CanceledException
-
-                # TODO: Make this look like the image before re-enabling
-                # latent_image = unpack(img.float(), self.height, self.width)
-                # latent_image = latent_image.squeeze()  # Remove unnecessary dimensions
-                # flattened_tensor = latent_image.reshape(-1)  # Flatten to shape [48*128*128]
-
-                # # Create a new tensor of the required shape [255, 255, 3]
-                # latent_image = flattened_tensor[: 255 * 255 * 3].reshape(255, 255, 3)  # Reshape to RGB format
-
-                # # Convert to a NumPy array and then to a PIL Image
-                # image = Image.fromarray(latent_image.cpu().numpy().astype(np.uint8))
-
-                # (width, height) = image.size
-                # width *= 8
-                # height *= 8
-
-                # dataURL = image_to_dataURL(image, image_format="JPEG")
-
-                # # TODO: move this whole function to invocation context to properly reference these variables
-                # context._services.events.emit_invocation_denoise_progress(
-                #     context._data.queue_item,
-                #     context._data.invocation,
-                #     state,
-                #     ProgressImage(dataURL=dataURL, width=width, height=height),
-                # )
-
-            x = denoise(
-                model=transformer,
-                img=img,
-                img_ids=img_ids,
-                txt=t5_embeddings,
-                txt_ids=txt_ids,
-                vec=clip_embeddings,
-                timesteps=timesteps,
-                step_callback=step_callback,
-                guidance=self.guidance,
-            )
-
-        x = unpack(x.float(), self.height, self.width)
-
-        return x
-
-    def _run_vae_decoding(
-        self,
-        context: InvocationContext,
-        latents: torch.Tensor,
-    ) -> Image.Image:
-        vae_info = context.models.load(self.vae.vae)
-        with vae_info as vae:
-            assert isinstance(vae, AutoEncoder)
-            latents = latents.to(dtype=TorchDevice.choose_torch_dtype())
-            img = vae.decode(latents)
-
-        img = img.clamp(-1, 1)
-        img = rearrange(img[0], "c h w -> h w c")
-        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
-
-        return img_pil

invokeai/app/invocations/flux_vae_decode.py

@@ -0,0 +1,60 @@
+import torch
+from einops import rearrange
+from PIL import Image
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    LatentsField,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.model_manager.load.load_base import LoadedModel
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "flux_vae_decode",
+    title="FLUX VAE Decode",
+    tags=["latents", "image", "vae", "l2i", "flux"],
+    category="latents",
+    version="1.0.0",
+)
+class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Generates an image from latents."""
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+
+    def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
+        with vae_info as vae:
+            assert isinstance(vae, AutoEncoder)
+            latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype())
+            img = vae.decode(latents)
+
+        img = img.clamp(-1, 1)
+        img = rearrange(img[0], "c h w -> h w c")  # noqa: F821
+        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
+        return img_pil
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+        vae_info = context.models.load(self.vae.vae)
+        image = self._vae_decode(vae_info=vae_info, latents=latents)
+
+        TorchDevice.empty_cache()
+        image_dto = context.images.save(image=image)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/flux_vae_encode.py

@@ -0,0 +1,67 @@
+import einops
+import torch
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    Input,
+    InputField,
+)
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import LatentsOutput
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
+from invokeai.backend.model_manager import LoadedModel
+from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
+from invokeai.backend.util.devices import TorchDevice
+
+
+@invocation(
+    "flux_vae_encode",
+    title="FLUX VAE Encode",
+    tags=["latents", "image", "vae", "i2l", "flux"],
+    category="latents",
+    version="1.0.0",
+)
+class FluxVaeEncodeInvocation(BaseInvocation):
+    """Encodes an image into latents."""
+
+    image: ImageField = InputField(
+        description="The image to encode.",
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+
+    @staticmethod
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
+        # TODO(ryand): Expose seed parameter at the invocation level.
+        # TODO(ryand): Write a util function for generating random tensors that is consistent across devices / dtypes.
+        # There's a starting point in get_noise(...), but it needs to be extracted and generalized. This function
+        # should be used for VAE encode sampling.
+        generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
+        with vae_info as vae:
+            assert isinstance(vae, AutoEncoder)
+            image_tensor = image_tensor.to(
+                device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
+            )
+            latents = vae.encode(image_tensor, sample=True, generator=generator)
+            return latents
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        image = context.images.get_pil(self.image.image_name)
+
+        vae_info = context.models.load(self.vae.vae)
+
+        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
+        if image_tensor.dim() == 3:
+            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
+
+        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
+
+        latents = latents.to("cpu")
+        name = context.tensors.save(tensor=latents)
+        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)

invokeai/app/invocations/image.py

@@ -6,19 +6,13 @@ import cv2
 import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    Classification,
-    invocation,
-    invocation_output,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.constants import IMAGE_MODES
 from invokeai.app.invocations.fields import (
     ColorField,
     FieldDescriptions,
     ImageField,
     InputField,
-    OutputField,
     WithBoard,
     WithMetadata,
 )
@@ -1013,62 +1007,3 @@ class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
         image_dto = context.images.save(image=mask, image_category=ImageCategory.MASK)
 
         return ImageOutput.build(image_dto)
-
-
-@invocation_output("canvas_v2_mask_and_crop_output")
-class CanvasV2MaskAndCropOutput(ImageOutput):
-    offset_x: int = OutputField(description="The x offset of the image, after cropping")
-    offset_y: int = OutputField(description="The y offset of the image, after cropping")
-
-
-@invocation(
-    "canvas_v2_mask_and_crop",
-    title="Canvas V2 Mask and Crop",
-    tags=["image", "mask", "id"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class CanvasV2MaskAndCropInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Handles Canvas V2 image output masking and cropping"""
-
-    source_image: ImageField | None = InputField(
-        default=None,
-        description="The source image onto which the masked generated image is pasted. If omitted, the masked generated image is returned with transparency.",
-    )
-    generated_image: ImageField = InputField(description="The image to apply the mask to")
-    mask: ImageField = InputField(description="The mask to apply")
-    mask_blur: int = InputField(default=0, ge=0, description="The amount to blur the mask by")
-
-    def _prepare_mask(self, mask: Image.Image) -> Image.Image:
-        mask_array = numpy.array(mask)
-        kernel = numpy.ones((self.mask_blur, self.mask_blur), numpy.uint8)
-        dilated_mask_array = cv2.erode(mask_array, kernel, iterations=3)
-        dilated_mask = Image.fromarray(dilated_mask_array)
-        if self.mask_blur > 0:
-            mask = dilated_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
-        return ImageOps.invert(mask.convert("L"))
-
-    def invoke(self, context: InvocationContext) -> CanvasV2MaskAndCropOutput:
-        mask = self._prepare_mask(context.images.get_pil(self.mask.image_name))
-
-        if self.source_image:
-            generated_image = context.images.get_pil(self.generated_image.image_name)
-            source_image = context.images.get_pil(self.source_image.image_name)
-            source_image.paste(generated_image, (0, 0), mask)
-            image_dto = context.images.save(image=source_image)
-        else:
-            generated_image = context.images.get_pil(self.generated_image.image_name)
-            generated_image.putalpha(mask)
-            image_dto = context.images.save(image=generated_image)
-
-        # bbox = image.getbbox()
-        # image = image.crop(bbox)
-
-        return CanvasV2MaskAndCropOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            offset_x=0,
-            offset_y=0,
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/mask.py

@@ -126,7 +126,7 @@ class ImageMaskToTensorInvocation(BaseInvocation, WithMetadata):
     title="Tensor Mask to Image",
     tags=["mask"],
     category="mask",
-    version="1.0.0",
+    version="1.1.0",
 )
 class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Convert a mask tensor to an image."""
@@ -135,6 +135,11 @@ class MaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
         mask = context.tensors.load(self.mask.tensor_name)
+
+        # Squeeze the channel dimension if it exists.
+        if mask.dim() == 3:
+            mask = mask.squeeze(0)
+
         # Ensure that the mask is binary.
         if mask.dtype != torch.bool:
             mask = mask > 0.5

invokeai/app/invocations/model.py

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

invokeai/app/services/events/events_common.py

@@ -88,7 +88,6 @@ class QueueItemEventBase(QueueEventBase):
 
     item_id: int = Field(description="The ID of the queue item")
     batch_id: str = Field(description="The ID of the queue batch")
-    origin: str | None = Field(default=None, description="The origin of the batch")
 
 
 class InvocationEventBase(QueueItemEventBase):
@@ -96,6 +95,8 @@ class InvocationEventBase(QueueItemEventBase):
 
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
     queue_id: str = Field(description="The ID of the queue")
+    item_id: int = Field(description="The ID of the queue item")
+    batch_id: str = Field(description="The ID of the queue batch")
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
     invocation: AnyInvocation = Field(description="The ID of the invocation")
     invocation_source_id: str = Field(description="The ID of the prepared invocation's source node")
@@ -113,7 +114,6 @@ class InvocationStartedEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -147,7 +147,6 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -185,7 +184,6 @@ class InvocationCompleteEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -218,7 +216,6 @@ class InvocationErrorEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -256,7 +253,6 @@ class QueueItemStatusChangedEvent(QueueItemEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             status=queue_item.status,
             error_type=queue_item.error_type,
@@ -283,14 +279,12 @@ class BatchEnqueuedEvent(QueueEventBase):
         description="The number of invocations initially requested to be enqueued (may be less than enqueued if queue was full)"
     )
     priority: int = Field(description="The priority of the batch")
-    origin: str | None = Field(default=None, description="The origin of the batch")
 
     @classmethod
     def build(cls, enqueue_result: EnqueueBatchResult) -> "BatchEnqueuedEvent":
         return cls(
             queue_id=enqueue_result.queue_id,
             batch_id=enqueue_result.batch.batch_id,
-            origin=enqueue_result.batch.origin,
             enqueued=enqueue_result.enqueued,
             requested=enqueue_result.requested,
             priority=enqueue_result.priority,

invokeai/app/services/session_queue/session_queue_base.py

@@ -6,7 +6,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -96,11 +95,6 @@ class SessionQueueBase(ABC):
         """Cancels all queue items with matching batch IDs"""
         pass
 
-    @abstractmethod
-    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
-        """Cancels all queue items with the given batch origin"""
-        pass
-
     @abstractmethod
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         """Cancels all queue items with matching queue ID"""

invokeai/app/services/session_queue/session_queue_common.py

@@ -77,7 +77,6 @@ BatchDataCollection: TypeAlias = list[list[BatchDatum]]
 
 class Batch(BaseModel):
     batch_id: str = Field(default_factory=uuid_string, description="The ID of the batch")
-    origin: str | None = Field(default=None, description="The origin of this batch.")
     data: Optional[BatchDataCollection] = Field(default=None, description="The batch data collection.")
     graph: Graph = Field(description="The graph to initialize the session with")
     workflow: Optional[WorkflowWithoutID] = Field(
@@ -196,7 +195,6 @@ class SessionQueueItemWithoutGraph(BaseModel):
     status: QUEUE_ITEM_STATUS = Field(default="pending", description="The status of this queue item")
     priority: int = Field(default=0, description="The priority of this queue item")
     batch_id: str = Field(description="The ID of the batch associated with this queue item")
-    origin: str | None = Field(default=None, description="The origin of this queue item. ")
     session_id: str = Field(
         description="The ID of the session associated with this queue item. The session doesn't exist in graph_executions until the queue item is executed."
     )
@@ -296,7 +294,6 @@ class SessionQueueStatus(BaseModel):
 class BatchStatus(BaseModel):
     queue_id: str = Field(..., description="The ID of the queue")
     batch_id: str = Field(..., description="The ID of the batch")
-    origin: str | None = Field(..., description="The origin of the batch")
     pending: int = Field(..., description="Number of queue items with status 'pending'")
     in_progress: int = Field(..., description="Number of queue items with status 'in_progress'")
     completed: int = Field(..., description="Number of queue items with status 'complete'")
@@ -331,12 +328,6 @@ class CancelByBatchIDsResult(BaseModel):
     canceled: int = Field(..., description="Number of queue items canceled")
 
 
-class CancelByOriginResult(BaseModel):
-    """Result of canceling by list of batch ids"""
-
-    canceled: int = Field(..., description="Number of queue items canceled")
-
-
 class CancelByQueueIDResult(CancelByBatchIDsResult):
     """Result of canceling by queue id"""
 
@@ -442,7 +433,6 @@ class SessionQueueValueToInsert(NamedTuple):
     field_values: Optional[str]  # field_values json
     priority: int  # priority
     workflow: Optional[str]  # workflow json
-    origin: str | None
 
 
 ValuesToInsert: TypeAlias = list[SessionQueueValueToInsert]
@@ -463,7 +453,6 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new
                 json.dumps(field_values, default=to_jsonable_python) if field_values else None,  # field_values (json)
                 priority,  # priority
                 json.dumps(workflow, default=to_jsonable_python) if workflow else None,  # workflow (json)
-                batch.origin,  # origin
             )
         )
     return values_to_insert

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -10,7 +10,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -128,8 +127,8 @@ class SqliteSessionQueue(SessionQueueBase):
 
             self.__cursor.executemany(
                 """--sql
-                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin)
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow)
+                VALUES (?, ?, ?, ?, ?, ?, ?)
                 """,
                 values_to_insert,
             )
@@ -418,7 +417,11 @@ class SqliteSessionQueue(SessionQueueBase):
             )
             self.__conn.commit()
             if current_queue_item is not None and current_queue_item.batch_id in batch_ids:
-                self._set_queue_item_status(current_queue_item.item_id, "canceled")
+                batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
+                queue_status = self.get_queue_status(queue_id=queue_id)
+                self.__invoker.services.events.emit_queue_item_status_changed(
+                    current_queue_item, batch_status, queue_status
+                )
         except Exception:
             self.__conn.rollback()
             raise
@@ -426,46 +429,6 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return CancelByBatchIDsResult(canceled=count)
 
-    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
-        try:
-            current_queue_item = self.get_current(queue_id)
-            self.__lock.acquire()
-            where = """--sql
-                WHERE
-                  queue_id == ?
-                  AND origin == ?
-                  AND status != 'canceled'
-                  AND status != 'completed'
-                  AND status != 'failed'
-                """
-            params = (queue_id, origin)
-            self.__cursor.execute(
-                f"""--sql
-                SELECT COUNT(*)
-                FROM session_queue
-                {where};
-                """,
-                params,
-            )
-            count = self.__cursor.fetchone()[0]
-            self.__cursor.execute(
-                f"""--sql
-                UPDATE session_queue
-                SET status = 'canceled'
-                {where};
-                """,
-                params,
-            )
-            self.__conn.commit()
-            if current_queue_item is not None and current_queue_item.origin == origin:
-                self._set_queue_item_status(current_queue_item.item_id, "canceled")
-        except Exception:
-            self.__conn.rollback()
-            raise
-        finally:
-            self.__lock.release()
-        return CancelByOriginResult(canceled=count)
-
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         try:
             current_queue_item = self.get_current(queue_id)
@@ -578,8 +541,7 @@ class SqliteSessionQueue(SessionQueueBase):
                     started_at,
                     session_id,
                     batch_id,
-                    queue_id,
-                    origin
+                    queue_id
                 FROM session_queue
                 WHERE queue_id = ?
             """
@@ -659,7 +621,7 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.acquire()
             self.__cursor.execute(
                 """--sql
-                SELECT status, count(*), origin
+                SELECT status, count(*)
                 FROM session_queue
                 WHERE
                   queue_id = ?
@@ -671,7 +633,6 @@ class SqliteSessionQueue(SessionQueueBase):
             result = cast(list[sqlite3.Row], self.__cursor.fetchall())
             total = sum(row[1] for row in result)
             counts: dict[str, int] = {row[0]: row[1] for row in result}
-            origin = result[0]["origin"] if result else None
         except Exception:
             self.__conn.rollback()
             raise
@@ -680,7 +641,6 @@ class SqliteSessionQueue(SessionQueueBase):
 
         return BatchStatus(
             batch_id=batch_id,
-            origin=origin,
             queue_id=queue_id,
             pending=counts.get("pending", 0),
             in_progress=counts.get("in_progress", 0),

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -17,7 +17,6 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_11 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_12 import build_migration_12
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_13 import build_migration_13
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_14 import build_migration_14
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_15 import build_migration_15
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -52,7 +51,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_12(app_config=config))
     migrator.register_migration(build_migration_13())
     migrator.register_migration(build_migration_14())
-    migrator.register_migration(build_migration_15())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_15.py

@@ -1,31 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration15Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._add_origin_col(cursor)
-
-    def _add_origin_col(self, cursor: sqlite3.Cursor) -> None:
-        """
-        - Adds `origin` column to the session queue table.
-        """
-
-        cursor.execute("ALTER TABLE session_queue ADD COLUMN origin TEXT;")
-
-
-def build_migration_15() -> Migration:
-    """
-    Build the migration from database version 14 to 15.
-
-    This migration does the following:
-        - Adds `origin` column to the session queue table.
-    """
-    migration_15 = Migration(
-        from_version=14,
-        to_version=15,
-        callback=Migration15Callback(),
-    )
-
-    return migration_15

invokeai/app/services/workflow_records/default_workflows/FLUX Image to Image.json

@@ -0,0 +1,397 @@
+{
+  "name": "FLUX Image to Image",
+  "author": "InvokeAI",
+  "description": "A simple image-to-image workflow using a FLUX dev model. ",
+  "version": "1.0.4",
+  "contact": "",
+  "tags": "image2image, flux, image-to-image",
+  "notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend using FLUX dev models for image-to-image workflows. The image-to-image performance with FLUX schnell models is poor.",
+  "exposedFields": [
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "model"
+    },
+    {
+      "nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "fieldName": "prompt"
+    },
+    {
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "t5_encoder_model"
+    },
+    {
+      "nodeId": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "fieldName": "denoising_start"
+    },
+    {
+      "nodeId": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "fieldName": "num_steps"
+    }
+  ],
+  "meta": {
+    "version": "3.0.0",
+    "category": "default"
+  },
+  "id": "0a91fc4c-5f93-469c-a6ad-e87bf47b68bf",
+  "nodes": [
+    {
+      "id": "2981a67c-480f-4237-9384-26b68dbf912b",
+      "type": "invocation",
+      "data": {
+        "id": "2981a67c-480f-4237-9384-26b68dbf912b",
+        "type": "flux_vae_encode",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": true,
+        "inputs": {
+          "image": {
+            "name": "image",
+            "label": ""
+          },
+          "vae": {
+            "name": "vae",
+            "label": ""
+          }
+        }
+      },
+      "position": {
+        "x": 732.7680166609682,
+        "y": -24.37398171806909
+      }
+    },
+    {
+      "id": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "type": "invocation",
+      "data": {
+        "id": "ace0258f-67d7-4eee-a218-6fff27065214",
+        "type": "flux_denoise",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": true,
+        "inputs": {
+          "board": {
+            "name": "board",
+            "label": ""
+          },
+          "metadata": {
+            "name": "metadata",
+            "label": ""
+          },
+          "latents": {
+            "name": "latents",
+            "label": ""
+          },
+          "denoise_mask": {
+            "name": "denoise_mask",
+            "label": ""
+          },
+          "denoising_start": {
+            "name": "denoising_start",
+            "label": "",
+            "value": 0.04
+          },
+          "denoising_end": {
+            "name": "denoising_end",
+            "label": "",
+            "value": 1
+          },
+          "transformer": {
+            "name": "transformer",
+            "label": ""
+          },
+          "positive_text_conditioning": {
+            "name": "positive_text_conditioning",
+            "label": ""
+          },
+          "width": {
+            "name": "width",
+            "label": "",
+            "value": 1024
+          },
+          "height": {
+            "name": "height",
+            "label": "",
+            "value": 1024
+          },
+          "num_steps": {
+            "name": "num_steps",
+            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
+            "value": 30
+          },
+          "guidance": {
+            "name": "guidance",
+            "label": "",
+            "value": 4
+          },
+          "seed": {
+            "name": "seed",
+            "label": "",
+            "value": 0
+          }
+        }
+      },
+      "position": {
+        "x": 1182.8836633018684,
+        "y": -251.38882958913183
+      }
+    },
+    {
+      "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+      "type": "invocation",
+      "data": {
+        "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+        "type": "flux_vae_decode",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": false,
+        "useCache": true,
+        "inputs": {
+          "board": {
+            "name": "board",
+            "label": ""
+          },
+          "metadata": {
+            "name": "metadata",
+            "label": ""
+          },
+          "latents": {
+            "name": "latents",
+            "label": ""
+          },
+          "vae": {
+            "name": "vae",
+            "label": ""
+          }
+        }
+      },
+      "position": {
+        "x": 1575.5797431839133,
+        "y": -209.00150975507415
+      }
+    },
+    {
+      "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "type": "invocation",
+      "data": {
+        "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+        "type": "flux_model_loader",
+        "version": "1.0.4",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": false,
+        "inputs": {
+          "model": {
+            "name": "model",
+            "label": "Model (dev variant recommended for Image-to-Image)"
+          },
+          "t5_encoder_model": {
+            "name": "t5_encoder_model",
+            "label": ""
+          },
+          "clip_embed_model": {
+            "name": "clip_embed_model",
+            "label": "",
+            "value": {
+              "key": "076fa0b4-6e06-413a-bfca-61ab6f8b26db",
+              "hash": "blake3:17c19f0ef941c3b7609a9c94a659ca5364de0be364a91d4179f0e39ba17c3b70",
+              "name": "clip-vit-large-patch14",
+              "base": "any",
+              "type": "clip_embed"
+            }
+          },
+          "vae_model": {
+            "name": "vae_model",
+            "label": "",
+            "value": {
+              "key": "0bfe1765-2895-4a93-87e3-00277c04545d",
+              "hash": "blake3:ce21cb76364aa6e2421311cf4a4b5eb052a76c4f1cd207b50703d8978198a068",
+              "name": "FLUX.1-schnell_ae",
+              "base": "flux",
+              "type": "vae"
+            }
+          }
+        }
+      },
+      "position": {
+        "x": 328.1809894659957,
+        "y": -90.2241133566946
+      }
+    },
+    {
+      "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "type": "invocation",
+      "data": {
+        "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+        "type": "flux_text_encoder",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": true,
+        "inputs": {
+          "clip": {
+            "name": "clip",
+            "label": ""
+          },
+          "t5_encoder": {
+            "name": "t5_encoder",
+            "label": ""
+          },
+          "t5_max_seq_len": {
+            "name": "t5_max_seq_len",
+            "label": "T5 Max Seq Len",
+            "value": 256
+          },
+          "prompt": {
+            "name": "prompt",
+            "label": "",
+            "value": "a cat wearing a birthday hat"
+          }
+        }
+      },
+      "position": {
+        "x": 745.8823365057267,
+        "y": -299.60249175851914
+      }
+    },
+    {
+      "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
+      "type": "invocation",
+      "data": {
+        "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
+        "type": "rand_int",
+        "version": "1.0.1",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": false,
+        "inputs": {
+          "low": {
+            "name": "low",
+            "label": "",
+            "value": 0
+          },
+          "high": {
+            "name": "high",
+            "label": "",
+            "value": 2147483647
+          }
+        }
+      },
+      "position": {
+        "x": 725.834098928012,
+        "y": 496.2710031089931
+      }
+    }
+  ],
+  "edges": [
+    {
+      "id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912bheight-ace0258f-67d7-4eee-a218-6fff27065214height",
+      "type": "default",
+      "source": "2981a67c-480f-4237-9384-26b68dbf912b",
+      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "sourceHandle": "height",
+      "targetHandle": "height"
+    },
+    {
+      "id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912bwidth-ace0258f-67d7-4eee-a218-6fff27065214width",
+      "type": "default",
+      "source": "2981a67c-480f-4237-9384-26b68dbf912b",
+      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "sourceHandle": "width",
+      "targetHandle": "width"
+    },
+    {
+      "id": "reactflow__edge-2981a67c-480f-4237-9384-26b68dbf912blatents-ace0258f-67d7-4eee-a218-6fff27065214latents",
+      "type": "default",
+      "source": "2981a67c-480f-4237-9384-26b68dbf912b",
+      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "sourceHandle": "latents",
+      "targetHandle": "latents"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-2981a67c-480f-4237-9384-26b68dbf912bvae",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "2981a67c-480f-4237-9384-26b68dbf912b",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    },
+    {
+      "id": "reactflow__edge-ace0258f-67d7-4eee-a218-6fff27065214latents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
+      "type": "default",
+      "source": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+      "sourceHandle": "latents",
+      "targetHandle": "latents"
+    },
+    {
+      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-ace0258f-67d7-4eee-a218-6fff27065214seed",
+      "type": "default",
+      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
+      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "sourceHandle": "value",
+      "targetHandle": "seed"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-ace0258f-67d7-4eee-a218-6fff27065214transformer",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "sourceHandle": "transformer",
+      "targetHandle": "transformer"
+    },
+    {
+      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-ace0258f-67d7-4eee-a218-6fff27065214positive_text_conditioning",
+      "type": "default",
+      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "target": "ace0258f-67d7-4eee-a218-6fff27065214",
+      "sourceHandle": "conditioning",
+      "targetHandle": "positive_text_conditioning"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-7e5172eb-48c1-44db-a770-8fd83e1435d1vae",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90max_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "sourceHandle": "max_seq_len",
+      "targetHandle": "t5_max_seq_len"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90t5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "sourceHandle": "t5_encoder",
+      "targetHandle": "t5_encoder"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90clip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "sourceHandle": "clip",
+      "targetHandle": "clip"
+    }
+  ]
+}

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

@@ -1,14 +1,14 @@
 {
   "name": "FLUX Text to Image",
   "author": "InvokeAI",
-  "description": "A simple text-to-image workflow using FLUX dev or schnell models. Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
-  "version": "1.0.0",
+  "description": "A simple text-to-image workflow using FLUX dev or schnell models.",
+  "version": "1.0.4",
   "contact": "",
   "tags": "text2image, flux",
   "notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
   "exposedFields": [
     {
-      "nodeId": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
       "fieldName": "model"
     },
     {
@@ -16,12 +16,12 @@
       "fieldName": "prompt"
     },
     {
-      "nodeId": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "fieldName": "num_steps"
+      "nodeId": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "fieldName": "t5_encoder_model"
     },
     {
-      "nodeId": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "fieldName": "t5_encoder"
+      "nodeId": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "fieldName": "num_steps"
     }
   ],
   "meta": {
@@ -30,12 +30,127 @@
   },
   "nodes": [
     {
-      "id": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+      "id": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
       "type": "invocation",
       "data": {
-        "id": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+        "id": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+        "type": "flux_denoise",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": true,
+        "useCache": true,
+        "inputs": {
+          "board": {
+            "name": "board",
+            "label": ""
+          },
+          "metadata": {
+            "name": "metadata",
+            "label": ""
+          },
+          "latents": {
+            "name": "latents",
+            "label": ""
+          },
+          "denoise_mask": {
+            "name": "denoise_mask",
+            "label": ""
+          },
+          "denoising_start": {
+            "name": "denoising_start",
+            "label": "",
+            "value": 0
+          },
+          "denoising_end": {
+            "name": "denoising_end",
+            "label": "",
+            "value": 1
+          },
+          "transformer": {
+            "name": "transformer",
+            "label": ""
+          },
+          "positive_text_conditioning": {
+            "name": "positive_text_conditioning",
+            "label": ""
+          },
+          "width": {
+            "name": "width",
+            "label": "",
+            "value": 1024
+          },
+          "height": {
+            "name": "height",
+            "label": "",
+            "value": 1024
+          },
+          "num_steps": {
+            "name": "num_steps",
+            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
+            "value": 30
+          },
+          "guidance": {
+            "name": "guidance",
+            "label": "",
+            "value": 4
+          },
+          "seed": {
+            "name": "seed",
+            "label": "",
+            "value": 0
+          }
+        }
+      },
+      "position": {
+        "x": 1186.1868226120378,
+        "y": -214.9459927686657
+      }
+    },
+    {
+      "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+      "type": "invocation",
+      "data": {
+        "id": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+        "type": "flux_vae_decode",
+        "version": "1.0.0",
+        "label": "",
+        "notes": "",
+        "isOpen": true,
+        "isIntermediate": false,
+        "useCache": true,
+        "inputs": {
+          "board": {
+            "name": "board",
+            "label": ""
+          },
+          "metadata": {
+            "name": "metadata",
+            "label": ""
+          },
+          "latents": {
+            "name": "latents",
+            "label": ""
+          },
+          "vae": {
+            "name": "vae",
+            "label": ""
+          }
+        }
+      },
+      "position": {
+        "x": 1575.5797431839133,
+        "y": -209.00150975507415
+      }
+    },
+    {
+      "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "type": "invocation",
+      "data": {
+        "id": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
         "type": "flux_model_loader",
-        "version": "1.0.3",
+        "version": "1.0.4",
         "label": "",
         "notes": "",
         "isOpen": true,
@@ -44,31 +159,25 @@
         "inputs": {
           "model": {
             "name": "model",
-            "label": "Model (Starter Models can be found in Model Manager)",
-            "value": {
-              "key": "f04a7a2f-c74d-4538-8d5e-879a53501662",
-              "hash": "random:4875da7a9508444ffa706f61961c260d0c6729f6181a86b31fad06df1277b850",
-              "name": "FLUX Dev (Quantized)",
-              "base": "flux",
-              "type": "main"
-            }
+            "label": ""
           },
-          "t5_encoder": {
-            "name": "t5_encoder",
-            "label": "T 5 Encoder (Starter Models can be found in Model Manager)",
-            "value": {
-              "key": "20dcd9ec-5fbb-4012-8401-049e707da5e5",
-              "hash": "random:f986be43ff3502169e4adbdcee158afb0e0a65a1edc4cab16ae59963630cfd8f",
-              "name": "t5_bnb_int8_quantized_encoder",
-              "base": "any",
-              "type": "t5_encoder"
-            }
+          "t5_encoder_model": {
+            "name": "t5_encoder_model",
+            "label": ""
+          },
+          "clip_embed_model": {
+            "name": "clip_embed_model",
+            "label": ""
+          },
+          "vae_model": {
+            "name": "vae_model",
+            "label": ""
           }
         }
       },
       "position": {
-        "x": 337.09365228062825,
-        "y": 40.63469521079861
+        "x": 381.1882713063478,
+        "y": -95.89663532854017
       }
     },
     {
@@ -105,8 +214,8 @@
         }
       },
       "position": {
-        "x": 824.1970602278849,
-        "y": 146.98251001061735
+        "x": 778.4899149328337,
+        "y": -100.36469216659502
       }
     },
     {
@@ -135,132 +244,75 @@
         }
       },
       "position": {
-        "x": 822.9899179655476,
-        "y": 360.9657214885052
-      }
-    },
-    {
-      "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "type": "invocation",
-      "data": {
-        "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-        "type": "flux_text_to_image",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": false,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "transformer": {
-            "name": "transformer",
-            "label": ""
-          },
-          "vae": {
-            "name": "vae",
-            "label": ""
-          },
-          "positive_text_conditioning": {
-            "name": "positive_text_conditioning",
-            "label": ""
-          },
-          "width": {
-            "name": "width",
-            "label": "",
-            "value": 1024
-          },
-          "height": {
-            "name": "height",
-            "label": "",
-            "value": 1024
-          },
-          "num_steps": {
-            "name": "num_steps",
-            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
-            "value": 30
-          },
-          "guidance": {
-            "name": "guidance",
-            "label": "",
-            "value": 4
-          },
-          "seed": {
-            "name": "seed",
-            "label": "",
-            "value": 0
-          }
-        }
-      },
-      "position": {
-        "x": 1216.3900791301849,
-        "y": 5.500841807102248
+        "x": 800.9667463219505,
+        "y": 285.8297267547506
       }
     }
   ],
   "edges": [
     {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33amax_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90transformer-4fe24f07-f906-4f55-ab2c-9beee56ef5bdtransformer",
       "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "sourceHandle": "transformer",
+      "targetHandle": "transformer"
+    },
+    {
+      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-4fe24f07-f906-4f55-ab2c-9beee56ef5bdpositive_text_conditioning",
+      "type": "default",
+      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
+      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "sourceHandle": "conditioning",
+      "targetHandle": "positive_text_conditioning"
+    },
+    {
+      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-4fe24f07-f906-4f55-ab2c-9beee56ef5bdseed",
+      "type": "default",
+      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
+      "target": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "sourceHandle": "value",
+      "targetHandle": "seed"
+    },
+    {
+      "id": "reactflow__edge-4fe24f07-f906-4f55-ab2c-9beee56ef5bdlatents-7e5172eb-48c1-44db-a770-8fd83e1435d1latents",
+      "type": "default",
+      "source": "4fe24f07-f906-4f55-ab2c-9beee56ef5bd",
+      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+      "sourceHandle": "latents",
+      "targetHandle": "latents"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90vae-7e5172eb-48c1-44db-a770-8fd83e1435d1vae",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
+      "target": "7e5172eb-48c1-44db-a770-8fd83e1435d1",
+      "sourceHandle": "vae",
+      "targetHandle": "vae"
+    },
+    {
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90max_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
+      "type": "default",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "max_seq_len",
       "targetHandle": "t5_max_seq_len"
     },
     {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33avae-159bdf1b-79e7-4174-b86e-d40e646964c8vae",
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90t5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
       "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "sourceHandle": "vae",
-      "targetHandle": "vae"
-    },
-    {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33atransformer-159bdf1b-79e7-4174-b86e-d40e646964c8transformer",
-      "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "sourceHandle": "transformer",
-      "targetHandle": "transformer"
-    },
-    {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33at5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
-      "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "t5_encoder",
       "targetHandle": "t5_encoder"
     },
     {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33aclip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
+      "id": "reactflow__edge-f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90clip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
       "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
+      "source": "f8d9d7c8-9ed7-4bd7-9e42-ab0e89bfac90",
       "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
       "sourceHandle": "clip",
       "targetHandle": "clip"
-    },
-    {
-      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-159bdf1b-79e7-4174-b86e-d40e646964c8positive_text_conditioning",
-      "type": "default",
-      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "sourceHandle": "conditioning",
-      "targetHandle": "positive_text_conditioning"
-    },
-    {
-      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-159bdf1b-79e7-4174-b86e-d40e646964c8seed",
-      "type": "default",
-      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
-      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "sourceHandle": "value",
-      "targetHandle": "seed"
     }
   ]
 }

invokeai/backend/flux/denoise.py

@@ -0,0 +1,45 @@
+from typing import Callable
+
+import torch
+from tqdm import tqdm
+
+from invokeai.backend.flux.inpaint_extension import InpaintExtension
+from invokeai.backend.flux.model import Flux
+
+
+def denoise(
+    model: Flux,
+    # model input
+    img: torch.Tensor,
+    img_ids: torch.Tensor,
+    txt: torch.Tensor,
+    txt_ids: torch.Tensor,
+    vec: torch.Tensor,
+    # sampling parameters
+    timesteps: list[float],
+    step_callback: Callable[[], None],
+    guidance: float,
+    inpaint_extension: InpaintExtension | None,
+):
+    # guidance_vec is ignored for schnell.
+    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
+    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
+        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
+        pred = model(
+            img=img,
+            img_ids=img_ids,
+            txt=txt,
+            txt_ids=txt_ids,
+            y=vec,
+            timesteps=t_vec,
+            guidance=guidance_vec,
+        )
+
+        img = img + (t_prev - t_curr) * pred
+
+        if inpaint_extension is not None:
+            img = inpaint_extension.merge_intermediate_latents_with_init_latents(img, t_prev)
+
+        step_callback()
+
+    return img

invokeai/backend/flux/inpaint_extension.py

@@ -0,0 +1,35 @@
+import torch
+
+
+class InpaintExtension:
+    """A class for managing inpainting with FLUX."""
+
+    def __init__(self, init_latents: torch.Tensor, inpaint_mask: torch.Tensor, noise: torch.Tensor):
+        """Initialize InpaintExtension.
+
+        Args:
+            init_latents (torch.Tensor): The initial latents (i.e. un-noised at timestep 0). In 'packed' format.
+            inpaint_mask (torch.Tensor): A mask specifying which elements to inpaint. Range [0, 1]. Values of 1 will be
+                re-generated. Values of 0 will remain unchanged. Values between 0 and 1 can be used to blend the
+                inpainted region with the background. In 'packed' format.
+            noise (torch.Tensor): The noise tensor used to noise the init_latents. In 'packed' format.
+        """
+        assert init_latents.shape == inpaint_mask.shape == noise.shape
+        self._init_latents = init_latents
+        self._inpaint_mask = inpaint_mask
+        self._noise = noise
+
+    def merge_intermediate_latents_with_init_latents(
+        self, intermediate_latents: torch.Tensor, timestep: float
+    ) -> torch.Tensor:
+        """Merge the intermediate latents with the initial latents for the current timestep using the inpaint mask. I.e.
+        update the intermediate latents to keep the regions that are not being inpainted on the correct noise
+        trajectory.
+
+        This function should be called after each denoising step.
+        """
+        # Noise the init latents for the current timestep.
+        noised_init_latents = self._noise * timestep + (1.0 - timestep) * self._init_latents
+
+        # Merge the intermediate latents with the noised_init_latents using the inpaint_mask.
+        return intermediate_latents * self._inpaint_mask + noised_init_latents * (1.0 - self._inpaint_mask)

invokeai/backend/flux/modules/autoencoder.py

@@ -258,16 +258,17 @@ class Decoder(nn.Module):
 
 
 class DiagonalGaussian(nn.Module):
-    def __init__(self, sample: bool = True, chunk_dim: int = 1):
+    def __init__(self, chunk_dim: int = 1):
         super().__init__()
-        self.sample = sample
         self.chunk_dim = chunk_dim
 
-    def forward(self, z: Tensor) -> Tensor:
+    def forward(self, z: Tensor, sample: bool = True, generator: torch.Generator | None = None) -> Tensor:
         mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
-        if self.sample:
+        if sample:
             std = torch.exp(0.5 * logvar)
-            return mean + std * torch.randn_like(mean)
+            # Unfortunately, torch.randn_like(...) does not accept a generator argument at the time of writing, so we
+            # have to use torch.randn(...) instead.
+            return mean + std * torch.randn(size=mean.size(), generator=generator, dtype=mean.dtype, device=mean.device)
         else:
             return mean
 
@@ -297,8 +298,21 @@ class AutoEncoder(nn.Module):
         self.scale_factor = params.scale_factor
         self.shift_factor = params.shift_factor
 
-    def encode(self, x: Tensor) -> Tensor:
-        z = self.reg(self.encoder(x))
+    def encode(self, x: Tensor, sample: bool = True, generator: torch.Generator | None = None) -> Tensor:
+        """Run VAE encoding on input tensor x.
+
+        Args:
+            x (Tensor): Input image tensor. Shape: (batch_size, in_channels, height, width).
+            sample (bool, optional): If True, sample from the encoded distribution, else, return the distribution mean.
+                Defaults to True.
+            generator (torch.Generator | None, optional): Optional random number generator for reproducibility.
+                Defaults to None.
+
+        Returns:
+            Tensor: Encoded latent tensor. Shape: (batch_size, z_channels, latent_height, latent_width).
+        """
+
+        z = self.reg(self.encoder(x), sample=sample, generator=generator)
         z = self.scale_factor * (z - self.shift_factor)
         return z
 

invokeai/backend/flux/sampling.py

@@ -1,176 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-import math
-from typing import Callable
-
-import torch
-from einops import rearrange, repeat
-from torch import Tensor
-from tqdm import tqdm
-
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.modules.conditioner import HFEncoder
-
-
-def get_noise(
-    num_samples: int,
-    height: int,
-    width: int,
-    device: torch.device,
-    dtype: torch.dtype,
-    seed: int,
-):
-    # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
-    rand_device = "cpu"
-    rand_dtype = torch.float16
-    return torch.randn(
-        num_samples,
-        16,
-        # allow for packing
-        2 * math.ceil(height / 16),
-        2 * math.ceil(width / 16),
-        device=rand_device,
-        dtype=rand_dtype,
-        generator=torch.Generator(device=rand_device).manual_seed(seed),
-    ).to(device=device, dtype=dtype)
-
-
-def prepare(t5: HFEncoder, clip: HFEncoder, img: Tensor, prompt: str | list[str]) -> dict[str, Tensor]:
-    bs, c, h, w = img.shape
-    if bs == 1 and not isinstance(prompt, str):
-        bs = len(prompt)
-
-    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-    if img.shape[0] == 1 and bs > 1:
-        img = repeat(img, "1 ... -> bs ...", bs=bs)
-
-    img_ids = torch.zeros(h // 2, w // 2, 3)
-    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
-    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
-    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
-
-    if isinstance(prompt, str):
-        prompt = [prompt]
-    txt = t5(prompt)
-    if txt.shape[0] == 1 and bs > 1:
-        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
-    txt_ids = torch.zeros(bs, txt.shape[1], 3)
-
-    vec = clip(prompt)
-    if vec.shape[0] == 1 and bs > 1:
-        vec = repeat(vec, "1 ... -> bs ...", bs=bs)
-
-    return {
-        "img": img,
-        "img_ids": img_ids.to(img.device),
-        "txt": txt.to(img.device),
-        "txt_ids": txt_ids.to(img.device),
-        "vec": vec.to(img.device),
-    }
-
-
-def time_shift(mu: float, sigma: float, t: Tensor):
-    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
-
-
-def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
-    m = (y2 - y1) / (x2 - x1)
-    b = y1 - m * x1
-    return lambda x: m * x + b
-
-
-def get_schedule(
-    num_steps: int,
-    image_seq_len: int,
-    base_shift: float = 0.5,
-    max_shift: float = 1.15,
-    shift: bool = True,
-) -> list[float]:
-    # extra step for zero
-    timesteps = torch.linspace(1, 0, num_steps + 1)
-
-    # shifting the schedule to favor high timesteps for higher signal images
-    if shift:
-        # eastimate mu based on linear estimation between two points
-        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
-        timesteps = time_shift(mu, 1.0, timesteps)
-
-    return timesteps.tolist()
-
-
-def denoise(
-    model: Flux,
-    # model input
-    img: Tensor,
-    img_ids: Tensor,
-    txt: Tensor,
-    txt_ids: Tensor,
-    vec: Tensor,
-    # sampling parameters
-    timesteps: list[float],
-    step_callback: Callable[[], None],
-    guidance: float = 4.0,
-):
-    dtype = model.txt_in.bias.dtype
-
-    # TODO(ryand): This shouldn't be necessary if we manage the dtypes properly in the caller.
-    img = img.to(dtype=dtype)
-    img_ids = img_ids.to(dtype=dtype)
-    txt = txt.to(dtype=dtype)
-    txt_ids = txt_ids.to(dtype=dtype)
-    vec = vec.to(dtype=dtype)
-
-    # this is ignored for schnell
-    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
-    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
-        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
-        pred = model(
-            img=img,
-            img_ids=img_ids,
-            txt=txt,
-            txt_ids=txt_ids,
-            y=vec,
-            timesteps=t_vec,
-            guidance=guidance_vec,
-        )
-
-        img = img + (t_prev - t_curr) * pred
-        step_callback()
-
-    return img
-
-
-def unpack(x: Tensor, height: int, width: int) -> Tensor:
-    return rearrange(
-        x,
-        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
-        h=math.ceil(height / 16),
-        w=math.ceil(width / 16),
-        ph=2,
-        pw=2,
-    )
-
-
-def prepare_latent_img_patches(latent_img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-    """Convert an input image in latent space to patches for diffusion.
-
-    This implementation was extracted from:
-    https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/sampling.py#L32
-
-    Returns:
-        tuple[Tensor, Tensor]: (img, img_ids), as defined in the original flux repo.
-    """
-    bs, c, h, w = latent_img.shape
-
-    # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
-    img = rearrange(latent_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-    if img.shape[0] == 1 and bs > 1:
-        img = repeat(img, "1 ... -> bs ...", bs=bs)
-
-    # Generate patch position ids.
-    img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device)
-    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device)[:, None]
-    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device)[None, :]
-    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
-
-    return img, img_ids

invokeai/backend/flux/sampling_utils.py

@@ -0,0 +1,135 @@
+# Initially pulled from https://github.com/black-forest-labs/flux
+
+import math
+from typing import Callable
+
+import torch
+from einops import rearrange, repeat
+
+
+def get_noise(
+    num_samples: int,
+    height: int,
+    width: int,
+    device: torch.device,
+    dtype: torch.dtype,
+    seed: int,
+):
+    # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
+    rand_device = "cpu"
+    rand_dtype = torch.float16
+    return torch.randn(
+        num_samples,
+        16,
+        # allow for packing
+        2 * math.ceil(height / 16),
+        2 * math.ceil(width / 16),
+        device=rand_device,
+        dtype=rand_dtype,
+        generator=torch.Generator(device=rand_device).manual_seed(seed),
+    ).to(device=device, dtype=dtype)
+
+
+def time_shift(mu: float, sigma: float, t: torch.Tensor) -> torch.Tensor:
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+
+def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
+    m = (y2 - y1) / (x2 - x1)
+    b = y1 - m * x1
+    return lambda x: m * x + b
+
+
+def get_schedule(
+    num_steps: int,
+    image_seq_len: int,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+    shift: bool = True,
+) -> list[float]:
+    # extra step for zero
+    timesteps = torch.linspace(1, 0, num_steps + 1)
+
+    # shifting the schedule to favor high timesteps for higher signal images
+    if shift:
+        # estimate mu based on linear estimation between two points
+        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
+        timesteps = time_shift(mu, 1.0, timesteps)
+
+    return timesteps.tolist()
+
+
+def _find_last_index_ge_val(timesteps: list[float], val: float, eps: float = 1e-6) -> int:
+    """Find the last index in timesteps that is >= val.
+
+    We use epsilon-close equality to avoid potential floating point errors.
+    """
+    idx = len(list(filter(lambda t: t >= (val - eps), timesteps))) - 1
+    assert idx >= 0
+    return idx
+
+
+def clip_timestep_schedule(timesteps: list[float], denoising_start: float, denoising_end: float) -> list[float]:
+    """Clip the timestep schedule to the denoising range.
+
+    Args:
+        timesteps (list[float]): The original timestep schedule: [1.0, ..., 0.0].
+        denoising_start (float): A value in [0, 1] specifying the start of the denoising process. E.g. a value of 0.2
+            would mean that the denoising process start at the last timestep in the schedule >= 0.8.
+        denoising_end (float): A value in [0, 1] specifying the end of the denoising process. E.g. a value of 0.8 would
+            mean that the denoising process end at the last timestep in the schedule >= 0.2.
+
+    Returns:
+        list[float]: The clipped timestep schedule.
+    """
+    assert 0.0 <= denoising_start <= 1.0
+    assert 0.0 <= denoising_end <= 1.0
+    assert denoising_start <= denoising_end
+
+    t_start_val = 1.0 - denoising_start
+    t_end_val = 1.0 - denoising_end
+
+    t_start_idx = _find_last_index_ge_val(timesteps, t_start_val)
+    t_end_idx = _find_last_index_ge_val(timesteps, t_end_val)
+
+    clipped_timesteps = timesteps[t_start_idx : t_end_idx + 1]
+
+    return clipped_timesteps
+
+
+def unpack(x: torch.Tensor, height: int, width: int) -> torch.Tensor:
+    """Unpack flat array of patch embeddings to latent image."""
+    return rearrange(
+        x,
+        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
+        h=math.ceil(height / 16),
+        w=math.ceil(width / 16),
+        ph=2,
+        pw=2,
+    )
+
+
+def pack(x: torch.Tensor) -> torch.Tensor:
+    """Pack latent image to flattented array of patch embeddings."""
+    # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
+    return rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+
+
+def generate_img_ids(h: int, w: int, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
+    """Generate tensor of image position ids.
+
+    Args:
+        h (int): Height of image in latent space.
+        w (int): Width of image in latent space.
+        batch_size (int): Batch size.
+        device (torch.device): Device.
+        dtype (torch.dtype): dtype.
+
+    Returns:
+        torch.Tensor: Image position ids.
+    """
+    img_ids = torch.zeros(h // 2, w // 2, 3, device=device, dtype=dtype)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=device, dtype=dtype)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=device, dtype=dtype)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
+    return img_ids

invokeai/backend/model_manager/load/load_default.py

@@ -72,6 +72,7 @@ class ModelLoader(ModelLoaderBase):
             pass
 
         config.path = str(self._get_model_path(config))
+        self._ram_cache.make_room(self.get_size_fs(config, Path(config.path), submodel_type))
         loaded_model = self._load_model(config, submodel_type)
 
         self._ram_cache.put(

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

@@ -193,15 +193,6 @@ class ModelCacheBase(ABC, Generic[T]):
         """
         pass
 
-    @abstractmethod
-    def exists(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> bool:
-        """Return true if the model identified by key and submodel_type is in the cache."""
-        pass
-
     @abstractmethod
     def cache_size(self) -> int:
         """Get the total size of the models currently cached."""

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

@@ -1,22 +1,6 @@
 # Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Development team
 # TODO: Add Stalker's proper name to copyright
-"""
-Manage a RAM cache of diffusion/transformer models for fast switching.
-They are moved between GPU VRAM and CPU RAM as necessary. If the cache
-grows larger than a preset maximum, then the least recently used
-model will be cleared and (re)loaded from disk when next needed.
-
-The cache returns context manager generators designed to load the
-model into the GPU within the context, and unload outside the
-context. Use like this:
-
-   cache = ModelCache(max_cache_size=7.5)
-   with cache.get_model('runwayml/stable-diffusion-1-5') as SD1,
-          cache.get_model('stabilityai/stable-diffusion-2') as SD2:
-       do_something_in_GPU(SD1,SD2)
-
-
-"""
+""" """
 
 import gc
 import math
@@ -40,45 +24,64 @@ from invokeai.backend.model_manager.load.model_util import calc_model_size_by_da
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
-# Maximum size of the cache, in gigs
-# Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
-DEFAULT_MAX_CACHE_SIZE = 6.0
-
-# amount of GPU memory to hold in reserve for use by generations (GB)
-DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
-
-# actual size of a gig
-GIG = 1073741824
+# Size of a GB in bytes.
+GB = 2**30
 
 # Size of a MB in bytes.
 MB = 2**20
 
 
 class ModelCache(ModelCacheBase[AnyModel]):
-    """Implementation of ModelCacheBase."""
+    """A cache for managing models in memory.
+
+    The cache is based on two levels of model storage:
+    - execution_device: The device where most models are executed (typically "cuda", "mps", or "cpu").
+    - storage_device: The device where models are offloaded when not in active use (typically "cpu").
+
+    The model cache is based on the following assumptions:
+    - storage_device_mem_size > execution_device_mem_size
+    - disk_to_storage_device_transfer_time >> storage_device_to_execution_device_transfer_time
+
+    A copy of all models in the cache is always kept on the storage_device. A subset of the models also have a copy on
+    the execution_device.
+
+    Models are moved between the storage_device and the execution_device as necessary. Cache size limits are enforced
+    on both the storage_device and the execution_device. The execution_device cache uses a smallest-first offload
+    policy. The storage_device cache uses a least-recently-used (LRU) offload policy.
+
+    Note: Neither of these offload policies has really been compared against alternatives. It's likely that different
+    policies would be better, although the optimal policies are likely heavily dependent on usage patterns and HW
+    configuration.
+
+    The cache returns context manager generators designed to load the model into the execution device (often GPU) within
+    the context, and unload outside the context.
+
+    Example usage:
+    ```
+    cache = ModelCache(max_cache_size=7.5, max_vram_cache_size=6.0)
+    with cache.get_model('runwayml/stable-diffusion-1-5') as SD1:
+        do_something_on_gpu(SD1)
+    ```
+    """
 
     def __init__(
         self,
-        max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
-        max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
+        max_cache_size: float,
+        max_vram_cache_size: float,
         execution_device: torch.device = torch.device("cuda"),
         storage_device: torch.device = torch.device("cpu"),
-        precision: torch.dtype = torch.float16,
-        sequential_offload: bool = False,
         lazy_offloading: bool = True,
-        sha_chunksize: int = 16777216,
         log_memory_usage: bool = False,
         logger: Optional[Logger] = None,
     ):
         """
         Initialize the model RAM cache.
 
-        :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
+        :param max_cache_size: Maximum size of the storage_device cache in GBs.
+        :param max_vram_cache_size: Maximum size of the execution_device cache in GBs.
         :param execution_device: Torch device to load active model into [torch.device('cuda')]
         :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
-        :param precision: Precision for loaded models [torch.float16]
-        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
-        :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
+        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded.
         :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
             operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
             snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
@@ -86,7 +89,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
         """
         # allow lazy offloading only when vram cache enabled
         self._lazy_offloading = lazy_offloading and max_vram_cache_size > 0
-        self._precision: torch.dtype = precision
         self._max_cache_size: float = max_cache_size
         self._max_vram_cache_size: float = max_vram_cache_size
         self._execution_device: torch.device = execution_device
@@ -145,15 +147,6 @@ class ModelCache(ModelCacheBase[AnyModel]):
             total += cache_record.size
         return total
 
-    def exists(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> bool:
-        """Return true if the model identified by key and submodel_type is in the cache."""
-        key = self._make_cache_key(key, submodel_type)
-        return key in self._cached_models
-
     def put(
         self,
         key: str,
@@ -203,7 +196,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         # more stats
         if self.stats:
             stats_name = stats_name or key
-            self.stats.cache_size = int(self._max_cache_size * GIG)
+            self.stats.cache_size = int(self._max_cache_size * GB)
             self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
             self.stats.in_cache = len(self._cached_models)
             self.stats.loaded_model_sizes[stats_name] = max(
@@ -231,10 +224,13 @@ class ModelCache(ModelCacheBase[AnyModel]):
             return model_key
 
     def offload_unlocked_models(self, size_required: int) -> None:
-        """Move any unused models from VRAM."""
-        reserved = self._max_vram_cache_size * GIG
+        """Offload models from the execution_device to make room for size_required.
+
+        :param size_required: The amount of space to clear in the execution_device cache, in bytes.
+        """
+        reserved = self._max_vram_cache_size * GB
         vram_in_use = torch.cuda.memory_allocated() + size_required
-        self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM needed for models; max allowed={(reserved/GIG):.2f}GB")
+        self.logger.debug(f"{(vram_in_use/GB):.2f}GB VRAM needed for models; max allowed={(reserved/GB):.2f}GB")
         for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
             if vram_in_use <= reserved:
                 break
@@ -245,7 +241,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 cache_entry.loaded = False
                 vram_in_use = torch.cuda.memory_allocated() + size_required
                 self.logger.debug(
-                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
+                    f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GB):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GB):.2f}GB"
                 )
 
         TorchDevice.empty_cache()
@@ -303,7 +299,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
         self.logger.debug(
             f"Moved model '{cache_entry.key}' from {source_device} to"
             f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
-            f"Estimated model size: {(cache_entry.size/GIG):.3f} GB."
+            f"Estimated model size: {(cache_entry.size/GB):.3f} GB."
             f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
         )
 
@@ -326,14 +322,14 @@ class ModelCache(ModelCacheBase[AnyModel]):
                     f"Moving model '{cache_entry.key}' from {source_device} to"
                     f" {target_device} caused an unexpected change in VRAM usage. The model's"
                     " estimated size may be incorrect. Estimated model size:"
-                    f" {(cache_entry.size/GIG):.3f} GB.\n"
+                    f" {(cache_entry.size/GB):.3f} GB.\n"
                     f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
                 )
 
     def print_cuda_stats(self) -> None:
         """Log CUDA diagnostics."""
-        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
-        ram = "%4.2fG" % (self.cache_size() / GIG)
+        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GB)
+        ram = "%4.2fG" % (self.cache_size() / GB)
 
         in_ram_models = 0
         in_vram_models = 0
@@ -353,17 +349,20 @@ class ModelCache(ModelCacheBase[AnyModel]):
                 )
 
     def make_room(self, size: int) -> None:
-        """Make enough room in the cache to accommodate a new model of indicated size."""
-        # calculate how much memory this model will require
-        # multiplier = 2 if self.precision==torch.float32 else 1
+        """Make enough room in the cache to accommodate a new model of indicated size.
+
+        Note: This function deletes all of the cache's internal references to a model in order to free it. If there are
+        external references to the model, there's nothing that the cache can do about it, and those models will not be
+        garbage-collected.
+        """
         bytes_needed = size
-        maximum_size = self.max_cache_size * GIG  # stored in GB, convert to bytes
+        maximum_size = self.max_cache_size * GB  # stored in GB, convert to bytes
         current_size = self.cache_size()
 
         if current_size + bytes_needed > maximum_size:
             self.logger.debug(
-                f"Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional"
-                f" {(bytes_needed/GIG):.2f} GB"
+                f"Max cache size exceeded: {(current_size/GB):.2f}/{self.max_cache_size:.2f} GB, need an additional"
+                f" {(bytes_needed/GB):.2f} GB"
             )
 
         self.logger.debug(f"Before making_room: cached_models={len(self._cached_models)}")
@@ -380,7 +379,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
 
             if not cache_entry.locked:
                 self.logger.debug(
-                    f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
+                    f"Removing {model_key} from RAM cache to free at least {(size/GB):.2f} GB (-{(cache_entry.size/GB):.2f} GB)"
                 )
                 current_size -= cache_entry.size
                 models_cleared += 1

invokeai/backend/quantization/bnb_llm_int8.py

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

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

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

invokeai/backend/util/__init__.py

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

invokeai/backend/util/util.py

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

invokeai/frontend/web/.eslintrc.js

@@ -12,10 +12,6 @@ module.exports = {
     'i18next/no-literal-string': 'error',
     // https://eslint.org/docs/latest/rules/no-console
     'no-console': 'error',
-    // https://eslint.org/docs/latest/rules/no-promise-executor-return
-    'no-promise-executor-return': 'error',
-    // https://eslint.org/docs/latest/rules/require-await
-    'require-await': 'error',
   },
   overrides: [
     /**

invokeai/frontend/web/.storybook/ReduxInit.tsx

@@ -1,5 +1,5 @@
 import { PropsWithChildren, memo, useEffect } from 'react';
-import { modelChanged } from '../src/features/controlLayers/store/paramsSlice';
+import { modelChanged } from '../src/features/parameters/store/generationSlice';
 import { useAppDispatch } from '../src/app/store/storeHooks';
 import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
 /**
@@ -10,9 +10,7 @@ export const ReduxInit = memo((props: PropsWithChildren) => {
   const dispatch = useAppDispatch();
   useGlobalModifiersInit();
   useEffect(() => {
-    dispatch(
-      modelChanged({ model: { key: 'test_model', hash: 'some_hash', name: 'some name', base: 'sd-1', type: 'main' } })
-    );
+    dispatch(modelChanged({ key: 'test_model', hash: 'some_hash', name: 'some name', base: 'sd-1', type: 'main' }));
   }, []);
 
   return props.children;

invokeai/frontend/web/knip.ts

@@ -9,8 +9,6 @@ const config: KnipConfig = {
     'src/services/api/schema.ts',
     'src/features/nodes/types/v1/**',
     'src/features/nodes/types/v2/**',
-    // TODO(psyche): maybe we can clean up these utils after canvas v2 release
-    'src/features/controlLayers/konva/util.ts',
   ],
   ignoreBinaries: ['only-allow'],
   paths: {

invokeai/frontend/web/package.json

@@ -24,7 +24,7 @@
     "build": "pnpm run lint && vite build",
     "typegen": "node scripts/typegen.js",
     "preview": "vite preview",
-    "lint:knip": "knip --tags=-knipignore",
+    "lint:knip": "knip",
     "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:1 src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",
     "lint:prettier": "prettier --check .",
@@ -52,17 +52,17 @@
     }
   },
   "dependencies": {
+    "@chakra-ui/react-use-size": "^2.1.0",
     "@dagrejs/dagre": "^1.1.3",
     "@dagrejs/graphlib": "^2.2.3",
     "@dnd-kit/core": "^6.1.0",
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.0.20",
-    "@invoke-ai/ui-library": "^0.0.32",
+    "@invoke-ai/ui-library": "^0.0.29",
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
-    "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.1",
     "compare-versions": "^6.1.1",
     "dateformat": "^5.0.3",
@@ -74,8 +74,6 @@
     "jsondiffpatch": "^0.6.0",
     "konva": "^9.3.14",
     "lodash-es": "^4.17.21",
-    "lru-cache": "^11.0.0",
-    "nanoid": "^5.0.7",
     "nanostores": "^0.11.2",
     "new-github-issue-url": "^1.0.0",
     "overlayscrollbars": "^2.10.0",
@@ -90,6 +88,7 @@
     "react-hotkeys-hook": "4.5.0",
     "react-i18next": "^14.1.3",
     "react-icons": "^5.2.1",
+    "react-konva": "^18.2.10",
     "react-redux": "9.1.2",
     "react-resizable-panels": "^2.0.23",
     "react-select": "5.8.0",
@@ -103,9 +102,9 @@
     "roarr": "^7.21.1",
     "serialize-error": "^11.0.3",
     "socket.io-client": "^4.7.5",
-    "stable-hash": "^0.0.4",
     "use-debounce": "^10.0.2",
     "use-device-pixel-ratio": "^1.1.2",
+    "use-image": "^1.1.1",
     "uuid": "^10.0.0",
     "zod": "^3.23.8",
     "zod-validation-error": "^3.3.1"

invokeai/frontend/web/pnpm-lock.yaml

@@ -5,6 +5,9 @@ settings:
   excludeLinksFromLockfile: false
 
 dependencies:
+  '@chakra-ui/react-use-size':
+    specifier: ^2.1.0
+    version: 2.1.0(react@18.3.1)
   '@dagrejs/dagre':
     specifier: ^1.1.3
     version: 1.1.3
@@ -24,8 +27,8 @@ dependencies:
     specifier: ^5.0.20
     version: 5.0.20
   '@invoke-ai/ui-library':
-    specifier: ^0.0.32
-    version: 0.0.32(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^0.0.29
+    version: 0.0.29(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1)
   '@nanostores/react':
     specifier: ^0.7.3
     version: 0.7.3(nanostores@0.11.2)(react@18.3.1)
@@ -35,12 +38,9 @@ dependencies:
   '@roarr/browser-log-writer':
     specifier: ^1.3.0
     version: 1.3.0
-  async-mutex:
-    specifier: ^0.5.0
-    version: 0.5.0
   chakra-react-select:
     specifier: ^4.9.1
-    version: 4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.3)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
+    version: 4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.0)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
   compare-versions:
     specifier: ^6.1.1
     version: 6.1.1
@@ -71,12 +71,6 @@ dependencies:
   lodash-es:
     specifier: ^4.17.21
     version: 4.17.21
-  lru-cache:
-    specifier: ^11.0.0
-    version: 11.0.0
-  nanoid:
-    specifier: ^5.0.7
-    version: 5.0.7
   nanostores:
     specifier: ^0.11.2
     version: 0.11.2
@@ -119,6 +113,9 @@ dependencies:
   react-icons:
     specifier: ^5.2.1
     version: 5.2.1(react@18.3.1)
+  react-konva:
+    specifier: ^18.2.10
+    version: 18.2.10(konva@9.3.14)(react-dom@18.3.1)(react@18.3.1)
   react-redux:
     specifier: 9.1.2
     version: 9.1.2(@types/react@18.3.3)(react@18.3.1)(redux@5.0.1)
@@ -158,15 +155,15 @@ dependencies:
   socket.io-client:
     specifier: ^4.7.5
     version: 4.7.5
-  stable-hash:
-    specifier: ^0.0.4
-    version: 0.0.4
   use-debounce:
     specifier: ^10.0.2
     version: 10.0.2(react@18.3.1)
   use-device-pixel-ratio:
     specifier: ^1.1.2
     version: 1.1.2(react@18.3.1)
+  use-image:
+    specifier: ^1.1.1
+    version: 1.1.1(react-dom@18.3.1)(react@18.3.1)
   uuid:
     specifier: ^10.0.0
     version: 10.0.0
@@ -1752,13 +1749,6 @@ packages:
     dependencies:
       regenerator-runtime: 0.14.1
 
-  /@babel/runtime@7.25.4:
-    resolution: {integrity: sha512-DSgLeL/FNcpXuzav5wfYvHCGvynXkJbn3Zvc3823AEe9nPwW9IK4UoCSS5yGymmQzN0pCPvivtgS6/8U2kkm1w==}
-    engines: {node: '>=6.9.0'}
-    dependencies:
-      regenerator-runtime: 0.14.1
-    dev: false
-
   /@babel/template@7.24.0:
     resolution: {integrity: sha512-Bkf2q8lMB0AFpX0NFEqSbx1OkTHf0f+0j82mkw+ZpzBnkk7e9Ql0891vlfgi+kHwOk8tQjiQHpqh4LaSa0fKEA==}
     engines: {node: '>=6.9.0'}
@@ -1845,7 +1835,7 @@ packages:
       '@chakra-ui/react-use-controllable-state': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/transition': 2.1.0(framer-motion@10.18.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
@@ -1861,7 +1851,7 @@ packages:
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
       '@chakra-ui/spinner': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1879,7 +1869,7 @@ packages:
       '@chakra-ui/react-children-utils': 2.0.6(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1892,7 +1882,7 @@ packages:
       '@chakra-ui/react-children-utils': 2.0.6(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1912,7 +1902,7 @@ packages:
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
       '@chakra-ui/spinner': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1923,7 +1913,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1942,7 +1932,7 @@ packages:
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/visually-hidden': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@zag-js/focus-visible': 0.16.0
       react: 18.3.1
@@ -1965,7 +1955,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1984,7 +1974,7 @@ packages:
       '@chakra-ui/system': '>=2.0.0'
       react: '>=18'
     dependencies:
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -1999,13 +1989,13 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/css-reset@2.3.0(@emotion/react@11.13.3)(react@18.3.1):
+  /@chakra-ui/css-reset@2.3.0(@emotion/react@11.13.0)(react@18.3.1):
     resolution: {integrity: sha512-cQwwBy5O0jzvl0K7PLTLgp8ijqLPKyuEMiDXwYzl95seD3AoeuoCLyzZcJtVqaUZ573PiBdAbY/IlZcwDOItWg==}
     peerDependencies:
       '@emotion/react': '>=10.0.35'
       react: '>=18'
     dependencies:
-      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2038,7 +2028,7 @@ packages:
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2069,7 +2059,7 @@ packages:
       '@chakra-ui/react-types': 2.0.7(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2092,7 +2082,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2103,7 +2093,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2115,7 +2105,7 @@ packages:
     dependencies:
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2130,7 +2120,7 @@ packages:
       '@chakra-ui/react-children-utils': 2.0.6(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2146,7 +2136,7 @@ packages:
       '@chakra-ui/react-children-utils': 2.0.6(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2171,7 +2161,7 @@ packages:
       '@chakra-ui/breakpoint-utils': 2.0.8
       '@chakra-ui/react-env': 3.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2196,7 +2186,7 @@ packages:
       '@chakra-ui/react-use-outside-click': 2.2.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/transition': 2.1.0(framer-motion@10.18.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
@@ -2223,7 +2213,7 @@ packages:
       '@chakra-ui/react-use-outside-click': 2.2.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/transition': 2.1.0(framer-motion@11.3.24)(react@18.3.1)
       framer-motion: 11.3.24(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
@@ -2244,7 +2234,7 @@ packages:
       '@chakra-ui/react-types': 2.0.7(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/transition': 2.1.0(framer-motion@10.18.0)(react@18.3.1)
       aria-hidden: 1.2.4
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
@@ -2273,7 +2263,7 @@ packages:
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2297,7 +2287,7 @@ packages:
       '@chakra-ui/react-use-controllable-state': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2319,7 +2309,7 @@ packages:
       '@chakra-ui/react-use-focus-on-pointer-down': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
     dev: false
@@ -2354,11 +2344,11 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/provider@2.4.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react-dom@18.3.1)(react@18.3.1):
+  /@chakra-ui/provider@2.4.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react-dom@18.3.1)(react@18.3.1):
     resolution: {integrity: sha512-w0Tef5ZCJK1mlJorcSjItCSbyvVuqpvyWdxZiVQmE6fvSJR83wZof42ux0+sfWD+I7rHSfj+f9nzhNaEWClysw==}
     peerDependencies:
       '@emotion/react': ^11.0.0
@@ -2366,13 +2356,13 @@ packages:
       react: '>=18'
       react-dom: '>=18'
     dependencies:
-      '@chakra-ui/css-reset': 2.3.0(@emotion/react@11.13.3)(react@18.3.1)
+      '@chakra-ui/css-reset': 2.3.0(@emotion/react@11.13.0)(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/react-env': 3.1.0(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/utils': 2.0.15
-      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/styled': 11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1)
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
     dev: false
@@ -2388,7 +2378,7 @@ packages:
       '@chakra-ui/react-types': 2.0.7(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       '@zag-js/focus-visible': 0.16.0
       react: 18.3.1
     dev: false
@@ -2588,7 +2578,7 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/react@2.8.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.3)(framer-motion@10.18.0)(react-dom@18.3.1)(react@18.3.1):
+  /@chakra-ui/react@2.8.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(@types/react@18.3.3)(framer-motion@10.18.0)(react-dom@18.3.1)(react@18.3.1):
     resolution: {integrity: sha512-Hn0moyxxyCDKuR9ywYpqgX8dvjqwu9ArwpIb9wHNYjnODETjLwazgNIliCVBRcJvysGRiV51U2/JtJVrpeCjUQ==}
     peerDependencies:
       '@emotion/react': ^11.0.0
@@ -2607,7 +2597,7 @@ packages:
       '@chakra-ui/close-button': 2.1.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/control-box': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/counter': 2.1.0(react@18.3.1)
-      '@chakra-ui/css-reset': 2.3.0(@emotion/react@11.13.3)(react@18.3.1)
+      '@chakra-ui/css-reset': 2.3.0(@emotion/react@11.13.0)(react@18.3.1)
       '@chakra-ui/editable': 3.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/focus-lock': 2.1.0(@types/react@18.3.3)(react@18.3.1)
       '@chakra-ui/form-control': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
@@ -2626,7 +2616,7 @@ packages:
       '@chakra-ui/popper': 3.1.0(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/progress': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/provider': 2.4.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react-dom@18.3.1)(react@18.3.1)
+      '@chakra-ui/provider': 2.4.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/radio': 2.1.2(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-env': 3.1.0(react@18.3.1)
       '@chakra-ui/select': 2.1.2(@chakra-ui/system@2.6.2)(react@18.3.1)
@@ -2638,7 +2628,7 @@ packages:
       '@chakra-ui/stepper': 2.3.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/styled-system': 2.9.2
       '@chakra-ui/switch': 2.1.2(@chakra-ui/system@2.6.2)(framer-motion@10.18.0)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/table': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/tabs': 3.0.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/tag': 3.1.1(@chakra-ui/system@2.6.2)(react@18.3.1)
@@ -2650,8 +2640,8 @@ packages:
       '@chakra-ui/transition': 2.1.0(framer-motion@10.18.0)(react@18.3.1)
       '@chakra-ui/utils': 2.0.15
       '@chakra-ui/visually-hidden': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/styled': 11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
@@ -2667,7 +2657,7 @@ packages:
     dependencies:
       '@chakra-ui/form-control': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2684,7 +2674,7 @@ packages:
       '@chakra-ui/media-query': 3.3.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-use-previous': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2694,7 +2684,7 @@ packages:
       '@chakra-ui/system': '>=2.0.0'
       react: '>=18'
     dependencies:
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2714,7 +2704,7 @@ packages:
       '@chakra-ui/react-use-pan-event': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-size': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2725,7 +2715,7 @@ packages:
       react: '>=18'
     dependencies:
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2738,7 +2728,7 @@ packages:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2751,7 +2741,7 @@ packages:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2772,12 +2762,12 @@ packages:
     dependencies:
       '@chakra-ui/checkbox': 2.3.2(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
     dev: false
 
-  /@chakra-ui/system@2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1):
+  /@chakra-ui/system@2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1):
     resolution: {integrity: sha512-EGtpoEjLrUu4W1fHD+a62XR+hzC5YfsWm+6lO0Kybcga3yYEij9beegO0jZgug27V+Rf7vns95VPVP6mFd/DEQ==}
     peerDependencies:
       '@emotion/react': ^11.0.0
@@ -2790,8 +2780,8 @@ packages:
       '@chakra-ui/styled-system': 2.9.2
       '@chakra-ui/theme-utils': 2.0.21
       '@chakra-ui/utils': 2.0.15
-      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/styled': 11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1)
       react: 18.3.1
       react-fast-compare: 3.2.2
     dev: false
@@ -2804,7 +2794,7 @@ packages:
     dependencies:
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2823,7 +2813,7 @@ packages:
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-safe-layout-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2835,7 +2825,7 @@ packages:
     dependencies:
       '@chakra-ui/icon': 3.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/react-context': 2.1.0(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2847,7 +2837,7 @@ packages:
     dependencies:
       '@chakra-ui/form-control': 2.2.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -2898,7 +2888,7 @@ packages:
       '@chakra-ui/react-use-update-effect': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
       '@chakra-ui/styled-system': 2.9.2
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       '@chakra-ui/theme': 3.3.1(@chakra-ui/styled-system@2.9.2)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
@@ -2921,7 +2911,7 @@ packages:
       '@chakra-ui/react-use-event-listener': 2.1.0(react@18.3.1)
       '@chakra-ui/react-use-merge-refs': 2.1.0(react@18.3.1)
       '@chakra-ui/shared-utils': 2.0.5
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
@@ -2964,7 +2954,7 @@ packages:
       '@chakra-ui/system': '>=2.0.0'
       react: '>=18'
     dependencies:
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
       react: 18.3.1
     dev: false
 
@@ -3047,10 +3037,10 @@ packages:
     resolution: {integrity: sha512-y2WQb+oP8Jqvvclh8Q55gLUyb7UFvgv7eJfsj7td5TToBrIUtPay2kMrZi4xjq9qw2vD0ZR5fSho0yqoFgX7Rw==}
     dependencies:
       '@babel/helper-module-imports': 7.24.7
-      '@babel/runtime': 7.25.4
+      '@babel/runtime': 7.25.0
       '@emotion/hash': 0.9.2
       '@emotion/memoize': 0.9.0
-      '@emotion/serialize': 1.3.1
+      '@emotion/serialize': 1.3.0
       babel-plugin-macros: 3.1.0
       convert-source-map: 1.9.0
       escape-string-regexp: 4.0.0
@@ -3138,8 +3128,8 @@ packages:
       react: 18.3.1
     dev: false
 
-  /@emotion/react@11.13.3(@types/react@18.3.3)(react@18.3.1):
-    resolution: {integrity: sha512-lIsdU6JNrmYfJ5EbUCf4xW1ovy5wKQ2CkPRM4xogziOxH1nXxBSjpC9YqbFAP7circxMfYp+6x676BqWcEiixg==}
+  /@emotion/react@11.13.0(@types/react@18.3.3)(react@18.3.1):
+    resolution: {integrity: sha512-WkL+bw1REC2VNV1goQyfxjx1GYJkcc23CRQkXX+vZNLINyfI7o+uUn/rTGPt/xJ3bJHd5GcljgnxHf4wRw5VWQ==}
     peerDependencies:
       '@types/react': '*'
       react: '>=16.8.0'
@@ -3147,10 +3137,10 @@ packages:
       '@types/react':
         optional: true
     dependencies:
-      '@babel/runtime': 7.25.4
+      '@babel/runtime': 7.25.0
       '@emotion/babel-plugin': 11.12.0
       '@emotion/cache': 11.13.1
-      '@emotion/serialize': 1.3.1
+      '@emotion/serialize': 1.3.0
       '@emotion/use-insertion-effect-with-fallbacks': 1.1.0(react@18.3.1)
       '@emotion/utils': 1.4.0
       '@emotion/weak-memoize': 0.4.0
@@ -3171,12 +3161,12 @@ packages:
       csstype: 3.1.3
     dev: false
 
-  /@emotion/serialize@1.3.1:
-    resolution: {integrity: sha512-dEPNKzBPU+vFPGa+z3axPRn8XVDetYORmDC0wAiej+TNcOZE70ZMJa0X7JdeoM6q/nWTMZeLpN/fTnD9o8MQBA==}
+  /@emotion/serialize@1.3.0:
+    resolution: {integrity: sha512-jACuBa9SlYajnpIVXB+XOXnfJHyckDfe6fOpORIM6yhBDlqGuExvDdZYHDQGoDf3bZXGv7tNr+LpLjJqiEQ6EA==}
     dependencies:
       '@emotion/hash': 0.9.2
       '@emotion/memoize': 0.9.0
-      '@emotion/unitless': 0.10.0
+      '@emotion/unitless': 0.9.0
       '@emotion/utils': 1.4.0
       csstype: 3.1.3
     dev: false
@@ -3189,7 +3179,7 @@ packages:
     resolution: {integrity: sha512-fTBW9/8r2w3dXWYM4HCB1Rdp8NLibOw2+XELH5m5+AkWiL/KqYX6dc0kKYlaYyKjrQ6ds33MCdMPEwgs2z1rqg==}
     dev: false
 
-  /@emotion/styled@11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1):
+  /@emotion/styled@11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1):
     resolution: {integrity: sha512-tkzkY7nQhW/zC4hztlwucpT8QEZ6eUzpXDRhww/Eej4tFfO0FxQYWRyg/c5CCXa4d/f174kqeXYjuQRnhzf6dA==}
     peerDependencies:
       '@emotion/react': ^11.0.0-rc.0
@@ -3199,11 +3189,11 @@ packages:
       '@types/react':
         optional: true
     dependencies:
-      '@babel/runtime': 7.25.4
+      '@babel/runtime': 7.25.0
       '@emotion/babel-plugin': 11.12.0
       '@emotion/is-prop-valid': 1.3.0
-      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/serialize': 1.3.1
+      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/serialize': 1.3.0
       '@emotion/use-insertion-effect-with-fallbacks': 1.1.0(react@18.3.1)
       '@emotion/utils': 1.4.0
       '@types/react': 18.3.3
@@ -3212,14 +3202,14 @@ packages:
       - supports-color
     dev: false
 
-  /@emotion/unitless@0.10.0:
-    resolution: {integrity: sha512-dFoMUuQA20zvtVTuxZww6OHoJYgrzfKM1t52mVySDJnMSEa08ruEvdYQbhvyu6soU+NeLVd3yKfTfT0NeV6qGg==}
-    dev: false
-
   /@emotion/unitless@0.8.1:
     resolution: {integrity: sha512-KOEGMu6dmJZtpadb476IsZBclKvILjopjUii3V+7MnXIQCYh8W3NgNcgwo21n9LXZX6EDIKvqfjYxXebDwxKmQ==}
     dev: false
 
+  /@emotion/unitless@0.9.0:
+    resolution: {integrity: sha512-TP6GgNZtmtFaFcsOgExdnfxLLpRDla4Q66tnenA9CktvVSdNKDvMVuUah4QvWPIpNjrWsGg3qeGo9a43QooGZQ==}
+    dev: false
+
   /@emotion/use-insertion-effect-with-fallbacks@1.0.1(react@18.3.1):
     resolution: {integrity: sha512-jT/qyKZ9rzLErtrjGgdkMBn2OP8wl0G3sQlBb3YPryvKHsjvINUhVaPFfP+fpBcOkmrVOVEEHQFJ7nbj2TH2gw==}
     peerDependencies:
@@ -3571,8 +3561,8 @@ packages:
       prettier: 3.3.3
     dev: true
 
-  /@invoke-ai/ui-library@0.0.32(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-JxAoblrDu/cZ4ha9KO4ry5OWvyLUE1Dj28i+ciMaDNUpC/cN+IyiTbUBoFoPaoN5JP8Zpd/MYCcmF2qsziHDzg==}
+  /@invoke-ai/ui-library@0.0.29(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@fontsource-variable/inter@5.0.20)(@types/react@18.3.3)(i18next@23.12.2)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-7SYOaiEEKk9iHk0hg2R2yVxiuV3I1x6bDEv0R3Y2tCH/Aq5XDG2tR+d7SQAPqf5+za3S+qfNFjbjl7GvEMwqmA==}
     peerDependencies:
       '@fontsource-variable/inter': ^5.0.16
       react: ^18.2.0
@@ -3582,14 +3572,14 @@ packages:
       '@chakra-ui/icons': 2.1.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/layout': 2.3.1(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/portal': 2.1.0(react-dom@18.3.1)(react@18.3.1)
-      '@chakra-ui/react': 2.8.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(@types/react@18.3.3)(framer-motion@10.18.0)(react-dom@18.3.1)(react@18.3.1)
+      '@chakra-ui/react': 2.8.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(@types/react@18.3.3)(framer-motion@10.18.0)(react-dom@18.3.1)(react@18.3.1)
       '@chakra-ui/styled-system': 2.9.2
       '@chakra-ui/theme-tools': 2.1.2(@chakra-ui/styled-system@2.9.2)
-      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
-      '@emotion/styled': 11.13.0(@emotion/react@11.13.3)(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
+      '@emotion/styled': 11.13.0(@emotion/react@11.13.0)(@types/react@18.3.3)(react@18.3.1)
       '@fontsource-variable/inter': 5.0.20
       '@nanostores/react': 0.7.3(nanostores@0.11.2)(react@18.3.1)
-      chakra-react-select: 4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.3)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
+      chakra-react-select: 4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.0)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
       framer-motion: 10.18.0(react-dom@18.3.1)(react@18.3.1)
       lodash-es: 4.17.21
       nanostores: 0.11.2
@@ -5212,6 +5202,12 @@ packages:
       '@types/react': 18.3.3
     dev: true
 
+  /@types/react-reconciler@0.28.8:
+    resolution: {integrity: sha512-SN9c4kxXZonFhbX4hJrZy37yw9e7EIxcpHCxQv5JUS18wDE5ovkQKlqQEkufdJCCMfuI9BnjUJvhYeJ9x5Ra7g==}
+    dependencies:
+      '@types/react': 18.3.3
+    dev: false
+
   /@types/react-transition-group@4.4.10:
     resolution: {integrity: sha512-hT/+s0VQs2ojCX823m60m5f0sL5idt9SO6Tj6Dg+rdphGPIeJbJ6CxvBYkgkGKrYeDjvIpKTR38UzmtHJOGW3Q==}
     dependencies:
@@ -5781,7 +5777,7 @@ packages:
     resolution: {integrity: sha512-y+CcFFwelSXpLZk/7fMB2mUbGtX9lKycf1MWJ7CaTIERyitVlyQx6C+sxcROU2BAJ24OiZyK+8wj2i8AlBoS3A==}
     engines: {node: '>=10'}
     dependencies:
-      tslib: 2.7.0
+      tslib: 2.6.3
     dev: false
 
   /aria-query@5.3.0:
@@ -5907,12 +5903,6 @@ packages:
       tslib: 2.6.3
     dev: true
 
-  /async-mutex@0.5.0:
-    resolution: {integrity: sha512-1A94B18jkJ3DYq284ohPxoXbfTA5HsQ7/Mf4DEhcyLx3Bz27Rh59iScbB6EPiP+B+joue6YCxcMXSbFC1tZKwA==}
-    dependencies:
-      tslib: 2.6.3
-    dev: false
-
   /attr-accept@2.2.2:
     resolution: {integrity: sha512-7prDjvt9HmqiZ0cl5CRjtS84sEyhsHP2coDkaZKRKVfCDo9s7iw7ChVmar78Gu9pC4SoR/28wFu/G5JJhTnqEg==}
     engines: {node: '>=4'}
@@ -6133,7 +6123,7 @@ packages:
       type-detect: 4.0.8
     dev: true
 
-  /chakra-react-select@4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.3)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1):
+  /chakra-react-select@4.9.1(@chakra-ui/form-control@2.2.0)(@chakra-ui/icon@3.2.0)(@chakra-ui/layout@2.3.1)(@chakra-ui/media-query@3.3.0)(@chakra-ui/menu@2.2.1)(@chakra-ui/spinner@2.1.0)(@chakra-ui/system@2.6.2)(@emotion/react@11.13.0)(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1):
     resolution: {integrity: sha512-jmgfN+S/wnTaCp3pW30GYDIZ5J8jWcT1gIbhpw6RdKV+atm/U4/sT+gaHOHHhRL8xeaYip+iI/m8MPGREkve0w==}
     peerDependencies:
       '@chakra-ui/form-control': ^2.0.0
@@ -6153,8 +6143,8 @@ packages:
       '@chakra-ui/media-query': 3.3.0(@chakra-ui/system@2.6.2)(react@18.3.1)
       '@chakra-ui/menu': 2.2.1(@chakra-ui/system@2.6.2)(framer-motion@11.3.24)(react@18.3.1)
       '@chakra-ui/spinner': 2.1.0(@chakra-ui/system@2.6.2)(react@18.3.1)
-      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.3)(@emotion/styled@11.13.0)(react@18.3.1)
-      '@emotion/react': 11.13.3(@types/react@18.3.3)(react@18.3.1)
+      '@chakra-ui/system': 2.6.2(@emotion/react@11.13.0)(@emotion/styled@11.13.0)(react@18.3.1)
+      '@emotion/react': 11.13.0(@types/react@18.3.3)(react@18.3.1)
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
       react-select: 5.8.0(@types/react@18.3.3)(react-dom@18.3.1)(react@18.3.1)
@@ -7579,7 +7569,7 @@ packages:
     resolution: {integrity: sha512-QFaHbhv9WPUeLYBDe/PAuLKJ4Dd9OPvKs9xZBr3yLXnUrDNaVXKu2baDBXe3naPY30hgHYSsf2JW4jzas2mDEQ==}
     engines: {node: '>=10'}
     dependencies:
-      tslib: 2.7.0
+      tslib: 2.6.3
     dev: false
 
   /for-each@0.3.3:
@@ -7618,7 +7608,7 @@ packages:
     dependencies:
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
-      tslib: 2.7.0
+      tslib: 2.6.3
     optionalDependencies:
       '@emotion/is-prop-valid': 0.8.8
     dev: false
@@ -8394,6 +8384,15 @@ packages:
       set-function-name: 2.0.2
     dev: true
 
+  /its-fine@1.2.5(react@18.3.1):
+    resolution: {integrity: sha512-fXtDA0X0t0eBYAGLVM5YsgJGsJ5jEmqZEPrGbzdf5awjv0xE7nqv3TVnvtUF060Tkes15DbDAKW/I48vsb6SyA==}
+    peerDependencies:
+      react: '>=18.0'
+    dependencies:
+      '@types/react-reconciler': 0.28.8
+      react: 18.3.1
+    dev: false
+
   /jackspeak@2.3.6:
     resolution: {integrity: sha512-N3yCS/NegsOBokc8GAdM8UcmfsKiSS8cipheD/nivzr700H+nsMOxJjQnvwOcRYVuFkdH0wGUvW2WbXGmrZGbQ==}
     engines: {node: '>=14'}
@@ -8711,11 +8710,6 @@ packages:
     engines: {node: 14 || >=16.14}
     dev: true
 
-  /lru-cache@11.0.0:
-    resolution: {integrity: sha512-Qv32eSV1RSCfhY3fpPE2GNZ8jgM9X7rdAfemLWqTUxwiyIC4jJ6Sy0fZ8H+oLWevO6i4/bizg7c8d8i6bxrzbA==}
-    engines: {node: 20 || >=22}
-    dev: false
-
   /lru-cache@5.1.1:
     resolution: {integrity: sha512-KpNARQA3Iwv+jTA0utUVVbrh+Jlrr1Fv0e56GGzAFOXN7dk/FviaDW8LHmK52DlcH4WP2n6gI8vN1aesBFgo9w==}
     dependencies:
@@ -9003,12 +8997,6 @@ packages:
     hasBin: true
     dev: true
 
-  /nanoid@5.0.7:
-    resolution: {integrity: sha512-oLxFY2gd2IqnjcYyOXD8XGCftpGtZP2AbHbOkthDkvRywH5ayNtPVy9YlOPcHckXzbLTCHpkb7FB+yuxKV13pQ==}
-    engines: {node: ^18 || >=20}
-    hasBin: true
-    dev: false
-
   /nanostores@0.11.2:
     resolution: {integrity: sha512-6bucNxMJA5rNV554WQl+MWGng0QVMzlRgpKTHHfIbVLrhQ+yRXBychV9ECGVuuUfCMQPjfIG9bj8oJFZ9hYP/Q==}
     engines: {node: ^18.0.0 || >=20.0.0}
@@ -9626,7 +9614,7 @@ packages:
     peerDependencies:
       react: ^15.3.0 || ^16.0.0 || ^17.0.0 || ^18.0.0
     dependencies:
-      '@babel/runtime': 7.25.4
+      '@babel/runtime': 7.25.0
       react: 18.3.1
     dev: false
 
@@ -9721,7 +9709,7 @@ packages:
       '@types/react':
         optional: true
     dependencies:
-      '@babel/runtime': 7.25.4
+      '@babel/runtime': 7.25.0
       '@types/react': 18.3.3
       focus-lock: 1.3.5
       prop-types: 15.8.1
@@ -9783,7 +9771,7 @@ packages:
       react-native:
         optional: true
     dependencies:
-      '@babel/runtime': 7.25.4
+      '@babel/runtime': 7.25.0
       html-parse-stringify: 3.0.1
       i18next: 23.12.2
       react: 18.3.1
@@ -9813,6 +9801,33 @@ packages:
     resolution: {integrity: sha512-/LLMVyas0ljjAtoYiPqYiL8VWXzUUdThrmU5+n20DZv+a+ClRoevUzw5JxU+Ieh5/c87ytoTBV9G1FiKfNJdmg==}
     dev: true
 
+  /react-konva@18.2.10(konva@9.3.14)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-ohcX1BJINL43m4ynjZ24MxFI1syjBdrXhqVxYVDw2rKgr3yuS0x/6m1Y2Z4sl4T/gKhfreBx8KHisd0XC6OT1g==}
+    peerDependencies:
+      konva: ^8.0.1 || ^7.2.5 || ^9.0.0
+      react: '>=18.0.0'
+      react-dom: '>=18.0.0'
+    dependencies:
+      '@types/react-reconciler': 0.28.8
+      its-fine: 1.2.5(react@18.3.1)
+      konva: 9.3.14
+      react: 18.3.1
+      react-dom: 18.3.1(react@18.3.1)
+      react-reconciler: 0.29.2(react@18.3.1)
+      scheduler: 0.23.2
+    dev: false
+
+  /react-reconciler@0.29.2(react@18.3.1):
+    resolution: {integrity: sha512-zZQqIiYgDCTP/f1N/mAR10nJGrPD2ZR+jDSEsKWJHYC7Cm2wodlwbR3upZRdC3cjIjSlTLNVyO7Iu0Yy7t2AYg==}
+    engines: {node: '>=0.10.0'}
+    peerDependencies:
+      react: ^18.3.1
+    dependencies:
+      loose-envify: 1.4.0
+      react: 18.3.1
+      scheduler: 0.23.2
+    dev: false
+
   /react-redux@9.1.2(@types/react@18.3.3)(react@18.3.1)(redux@5.0.1):
     resolution: {integrity: sha512-0OA4dhM1W48l3uzmv6B7TXPCGmokUU4p1M44DGN2/D9a1FjVPukVjER1PcPX97jIg6aUeLq1XJo1IpfbgULn0w==}
     peerDependencies:
@@ -9845,7 +9860,7 @@ packages:
       '@types/react': 18.3.3
       react: 18.3.1
       react-style-singleton: 2.2.1(@types/react@18.3.3)(react@18.3.1)
-      tslib: 2.7.0
+      tslib: 2.6.3
     dev: false
 
   /react-remove-scroll@2.5.10(@types/react@18.3.3)(react@18.3.1):
@@ -9862,7 +9877,7 @@ packages:
       react: 18.3.1
       react-remove-scroll-bar: 2.3.6(@types/react@18.3.3)(react@18.3.1)
       react-style-singleton: 2.2.1(@types/react@18.3.3)(react@18.3.1)
-      tslib: 2.7.0
+      tslib: 2.6.3
       use-callback-ref: 1.3.2(@types/react@18.3.3)(react@18.3.1)
       use-sidecar: 1.1.2(@types/react@18.3.3)(react@18.3.1)
     dev: false
@@ -9912,7 +9927,7 @@ packages:
       get-nonce: 1.0.1
       invariant: 2.2.4
       react: 18.3.1
-      tslib: 2.7.0
+      tslib: 2.6.3
     dev: false
 
   /react-transition-group@4.4.5(react-dom@18.3.1)(react@18.3.1):
@@ -10610,10 +10625,6 @@ packages:
     resolution: {integrity: sha512-D9cPgkvLlV3t3IzL0D0YLvGA9Ahk4PcvVwUbN0dSGr1aP0Nrt4AEnTUbuGvquEC0mA64Gqt1fzirlRs5ibXx8g==}
     dev: true
 
-  /stable-hash@0.0.4:
-    resolution: {integrity: sha512-LjdcbuBeLcdETCrPn9i8AYAZ1eCtu4ECAWtP7UleOiZ9LzVxRzzUZEoZ8zB24nhkQnDWyET0I+3sWokSDS3E7g==}
-    dev: false
-
   /stack-generator@2.0.10:
     resolution: {integrity: sha512-mwnua/hkqM6pF4k8SnmZ2zfETsRUpWXREfA/goT8SLCV4iOFa4bzOX2nDipWAZFPTjLvQB82f5yaodMVhK0yJQ==}
     dependencies:
@@ -11052,10 +11063,6 @@ packages:
   /tslib@2.6.3:
     resolution: {integrity: sha512-xNvxJEOUiWPGhUuUdQgAJPKOOJfGnIyKySOc09XkKsgdUV/3E2zvwZYdejjmRgPCgcym1juLH3226yA7sEFJKQ==}
 
-  /tslib@2.7.0:
-    resolution: {integrity: sha512-gLXCKdN1/j47AiHiOkJN69hJmcbGTHI0ImLmbYLHykhgeN0jVGola9yVjFgzCUklsZQMW55o+dW7IXv3RCXDzA==}
-    dev: false
-
   /tsutils@3.21.0(typescript@5.5.4):
     resolution: {integrity: sha512-mHKK3iUXL+3UF6xL5k0PEhKRUBKPBCv/+RkEOpjRWxxx27KKRBmmA60A9pgOUvMi8GKhRMPEmjBRPzs2W7O1OA==}
     engines: {node: '>= 6'}
@@ -11303,7 +11310,7 @@ packages:
     dependencies:
       '@types/react': 18.3.3
       react: 18.3.1
-      tslib: 2.7.0
+      tslib: 2.6.3
     dev: false
 
   /use-debounce@10.0.2(react@18.3.1):
@@ -11323,6 +11330,16 @@ packages:
       react: 18.3.1
     dev: false
 
+  /use-image@1.1.1(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-n4YO2k8AJG/BcDtxmBx8Aa+47kxY5m335dJiCQA5tTeVU4XdhrhqR6wT0WISRXwdMEOv5CSjqekDZkEMiiWaYQ==}
+    peerDependencies:
+      react: '>=16.8.0'
+      react-dom: '>=16.8.0'
+    dependencies:
+      react: 18.3.1
+      react-dom: 18.3.1(react@18.3.1)
+    dev: false
+
   /use-isomorphic-layout-effect@1.1.2(@types/react@18.3.3)(react@18.3.1):
     resolution: {integrity: sha512-49L8yCO3iGT/ZF9QttjwLF/ZD9Iwto5LnH5LmEdk/6cFmXddqi2ulF0edxTwjj+7mqvpVVGQWvbXZdn32wRSHA==}
     peerDependencies:
@@ -11349,7 +11366,7 @@ packages:
       '@types/react': 18.3.3
       detect-node-es: 1.1.0
       react: 18.3.1
-      tslib: 2.7.0
+      tslib: 2.6.3
     dev: false
 
   /use-sync-external-store@1.2.0(react@18.3.1):

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

@@ -80,7 +80,6 @@
         "aboutDesc": "Using Invoke for work? Check out:",
         "aboutHeading": "Own Your Creative Power",
         "accept": "Accept",
-        "apply": "Apply",
         "add": "Add",
         "advanced": "Advanced",
         "ai": "ai",
@@ -116,7 +115,6 @@
         "githubLabel": "Github",
         "goTo": "Go to",
         "hotkeysLabel": "Hotkeys",
-        "loadingImage": "Loading Image",
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
         "inpaint": "inpaint",
@@ -327,10 +325,6 @@
         "canceled": "Canceled",
         "completedIn": "Completed in",
         "batch": "Batch",
-        "origin": "Origin",
-        "originCanvas": "Canvas",
-        "originWorkflows": "Workflows",
-        "originOther": "Other",
         "batchFieldValues": "Batch Field Values",
         "item": "Item",
         "session": "Session",
@@ -702,6 +696,8 @@
         "availableModels": "Available Models",
         "baseModel": "Base Model",
         "cancel": "Cancel",
+        "clipEmbed": "CLIP Embed",
+        "clipVision": "CLIP Vision",
         "config": "Config",
         "convert": "Convert",
         "convertingModelBegin": "Converting Model. Please wait.",
@@ -789,6 +785,7 @@
         "settings": "Settings",
         "simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
         "source": "Source",
+        "spandrelImageToImage": "Image to Image (Spandrel)",
         "starterModels": "Starter Models",
         "starterModelsInModelManager": "Starter Models can be found in Model Manager",
         "syncModels": "Sync Models",
@@ -797,6 +794,7 @@
         "loraTriggerPhrases": "LoRA Trigger Phrases",
         "mainModelTriggerPhrases": "Main Model Trigger Phrases",
         "typePhraseHere": "Type phrase here",
+        "t5Encoder": "T5 Encoder",
         "upcastAttention": "Upcast Attention",
         "uploadImage": "Upload Image",
         "urlOrLocalPath": "URL or Local Path",
@@ -1102,6 +1100,7 @@
         "confirmOnDelete": "Confirm On Delete",
         "developer": "Developer",
         "displayInProgress": "Display Progress Images",
+        "enableImageDebugging": "Enable Image Debugging",
         "enableInformationalPopovers": "Enable Informational Popovers",
         "informationalPopoversDisabled": "Informational Popovers Disabled",
         "informationalPopoversDisabledDesc": "Informational popovers have been disabled. Enable them in Settings.",
@@ -1568,7 +1567,7 @@
         "copyToClipboard": "Copy to Clipboard",
         "cursorPosition": "Cursor Position",
         "darkenOutsideSelection": "Darken Outside Selection",
-        "discardAll": "Discard All & Cancel Pending Generations",
+        "discardAll": "Discard All",
         "discardCurrent": "Discard Current",
         "downloadAsImage": "Download As Image",
         "enableMask": "Enable Mask",
@@ -1646,121 +1645,39 @@
         "storeNotInitialized": "Store is not initialized"
     },
     "controlLayers": {
-        "clearHistory": "Clear History",
-        "generateMode": "Generate",
-        "generateModeDesc": "Create individual images. Generated images are added directly to the gallery.",
-        "composeMode": "Compose",
-        "composeModeDesc": "Compose your work iterative. Generated images are added back to the canvas.",
-        "autoSave": "Auto-save to Gallery",
-        "resetCanvas": "Reset Canvas",
-        "resetAll": "Reset All",
-        "clearCaches": "Clear Caches",
-        "recalculateRects": "Recalculate Rects",
-        "clipToBbox": "Clip Strokes to Bbox",
+        "deleteAll": "Delete All",
         "addLayer": "Add Layer",
-        "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
         "moveToBack": "Move to Back",
         "moveForward": "Move Forward",
         "moveBackward": "Move Backward",
         "brushSize": "Brush Size",
-        "width": "Width",
-        "zoom": "Zoom",
-        "resetView": "Reset View",
         "controlLayers": "Control Layers",
         "globalMaskOpacity": "Global Mask Opacity",
         "autoNegative": "Auto Negative",
-        "enableAutoNegative": "Enable Auto Negative",
-        "disableAutoNegative": "Disable Auto Negative",
         "deletePrompt": "Delete Prompt",
         "resetRegion": "Reset Region",
         "debugLayers": "Debug Layers",
         "rectangle": "Rectangle",
-        "maskFill": "Mask Fill",
+        "maskPreviewColor": "Mask Preview Color",
         "addPositivePrompt": "Add $t(common.positivePrompt)",
         "addNegativePrompt": "Add $t(common.negativePrompt)",
         "addIPAdapter": "Add $t(common.ipAdapter)",
+        "regionalGuidance": "Regional Guidance",
         "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
-        "raster": "Raster",
-        "rasterLayer_one": "Raster Layer",
-        "controlLayer_one": "Control Layer",
-        "inpaintMask_one": "Inpaint Mask",
-        "regionalGuidance_one": "Regional Guidance",
-        "ipAdapter_one": "IP Adapter",
-        "rasterLayer_other": "Raster Layers",
-        "controlLayer_other": "Control Layers",
-        "inpaintMask_other": "Inpaint Masks",
-        "regionalGuidance_other": "Regional Guidance",
-        "ipAdapter_other": "IP Adapters",
         "opacity": "Opacity",
-        "regionalGuidance_withCount_hidden": "Regional Guidance ({{count}} hidden)",
-        "controlAdapters_withCount_hidden": "Control Adapters ({{count}} hidden)",
-        "controlLayers_withCount_hidden": "Control Layers ({{count}} hidden)",
-        "rasterLayers_withCount_hidden": "Raster Layers ({{count}} hidden)",
-        "ipAdapters_withCount_hidden": "IP Adapters ({{count}} hidden)",
-        "inpaintMasks_withCount_hidden": "Inpaint Masks ({{count}} hidden)",
-        "regionalGuidance_withCount_visible": "Regional Guidance ({{count}})",
-        "controlAdapters_withCount_visible": "Control Adapters ({{count}})",
-        "controlLayers_withCount_visible": "Control Layers ({{count}})",
-        "rasterLayers_withCount_visible": "Raster Layers ({{count}})",
-        "ipAdapters_withCount_visible": "IP Adapters ({{count}})",
-        "inpaintMasks_withCount_visible": "Inpaint Masks ({{count}})",
         "globalControlAdapter": "Global $t(controlnet.controlAdapter_one)",
         "globalControlAdapterLayer": "Global $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
         "globalIPAdapter": "Global $t(common.ipAdapter)",
         "globalIPAdapterLayer": "Global $t(common.ipAdapter) $t(unifiedCanvas.layer)",
         "globalInitialImage": "Global Initial Image",
         "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
-        "layer": "Layer",
         "opacityFilter": "Opacity Filter",
         "clearProcessor": "Clear Processor",
         "resetProcessor": "Reset Processor to Defaults",
         "noLayersAdded": "No Layers Added",
         "layers_one": "Layer",
-        "layers_other": "Layers",
-        "objects_zero": "empty",
-        "objects_one": "{{count}} object",
-        "objects_other": "{{count}} objects",
-        "convertToControlLayer": "Convert to Control Layer",
-        "convertToRasterLayer": "Convert to Raster Layer",
-        "transparency": "Transparency",
-        "enableTransparencyEffect": "Enable Transparency Effect",
-        "disableTransparencyEffect": "Disable Transparency Effect",
-        "hidingType": "Hiding {{type}}",
-        "showingType": "Showing {{type}}",
-        "dynamicGrid": "Dynamic Grid",
-        "logDebugInfo": "Log Debug Info",
-        "locked": "Locked",
-        "unlocked": "Unlocked",
-        "deleteSelected": "Delete Selected",
-        "deleteAll": "Delete All",
-        "fill": {
-            "fillStyle": "Fill Style",
-            "solid": "Solid",
-            "grid": "Grid",
-            "crosshatch": "Crosshatch",
-            "vertical": "Vertical",
-            "horizontal": "Horizontal",
-            "diagonal": "Diagonal"
-        },
-        "tool": {
-            "brush": "Brush",
-            "eraser": "Eraser",
-            "rectangle": "Rectangle",
-            "bbox": "Bbox",
-            "move": "Move",
-            "view": "View",
-            "transform": "Transform",
-            "colorPicker": "Color Picker"
-        },
-        "filter": {
-            "filter": "Filter",
-            "filters": "Filters",
-            "filterType": "Filter Type",
-            "preview": "Preview",
-            "apply": "Apply",
-            "cancel": "Cancel"
-        }
+        "layers_other": "Layers"
     },
     "upscaling": {
         "upscale": "Upscale",
@@ -1848,30 +1765,5 @@
             "upscaling": "Upscaling",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
-    },
-    "system": {
-        "enableLogging": "Enable Logging",
-        "logLevel": {
-            "logLevel": "Log Level",
-            "trace": "Trace",
-            "debug": "Debug",
-            "info": "Info",
-            "warn": "Warn",
-            "error": "Error",
-            "fatal": "Fatal"
-        },
-        "logNamespaces": {
-            "logNamespaces": "Log Namespaces",
-            "gallery": "Gallery",
-            "models": "Models",
-            "config": "Config",
-            "canvas": "Canvas",
-            "generation": "Generation",
-            "workflows": "Workflows",
-            "system": "System",
-            "events": "Events",
-            "queue": "Queue",
-            "metadata": "Metadata"
-        }
     }
 }

invokeai/frontend/web/scripts/typegen.js

@@ -38,7 +38,7 @@ async function generateTypes(schema) {
   process.stdout.write(`\nOK!\r\n`);
 }
 
-function main() {
+async function main() {
   const encoding = 'utf-8';
 
   if (process.stdin.isTTY) {

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

@@ -6,7 +6,6 @@ import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/ap
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import ImageUploadOverlay from 'common/components/ImageUploadOverlay';
-import { useScopeFocusWatcher } from 'common/hooks/interactionScopes';
 import { useClearStorage } from 'common/hooks/useClearStorage';
 import { useFullscreenDropzone } from 'common/hooks/useFullscreenDropzone';
 import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
@@ -14,13 +13,13 @@ import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardMo
 import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
 import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
-import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
 import { StylePresetModal } from 'features/stylePresets/components/StylePresetForm/StylePresetModal';
+import { activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { configChanged } from 'features/system/store/configSlice';
-import { selectLanguage } from 'features/system/store/systemSelectors';
-import { AppContent } from 'features/ui/components/AppContent';
+import { languageSelector } from 'features/system/store/systemSelectors';
+import InvokeTabs from 'features/ui/components/InvokeTabs';
+import type { InvokeTabName } from 'features/ui/store/tabMap';
 import { setActiveTab } from 'features/ui/store/uiSlice';
-import type { TabName } from 'features/ui/store/uiTypes';
 import { useGetAndLoadLibraryWorkflow } from 'features/workflowLibrary/hooks/useGetAndLoadLibraryWorkflow';
 import { AnimatePresence } from 'framer-motion';
 import i18n from 'i18n';
@@ -41,11 +40,18 @@ interface Props {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  destination?: TabName | undefined;
+  selectedStylePresetId?: string;
+  destination?: InvokeTabName | undefined;
 }
 
-const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, destination }: Props) => {
-  const language = useAppSelector(selectLanguage);
+const App = ({
+  config = DEFAULT_CONFIG,
+  selectedImage,
+  selectedWorkflowId,
+  selectedStylePresetId,
+  destination,
+}: Props) => {
+  const language = useAppSelector(languageSelector);
   const logger = useLogger('system');
   const dispatch = useAppDispatch();
   const clearStorage = useClearStorage();
@@ -83,6 +89,12 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, desti
     }
   }, [selectedWorkflowId, getAndLoadWorkflow]);
 
+  useEffect(() => {
+    if (selectedStylePresetId) {
+      dispatch(activeStylePresetIdChanged(selectedStylePresetId));
+    }
+  }, [dispatch, selectedStylePresetId]);
+
   useEffect(() => {
     if (destination) {
       dispatch(setActiveTab(destination));
@@ -95,7 +107,6 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, desti
 
   useStarterModelsToast();
   useSyncQueueStatus();
-  useScopeFocusWatcher();
 
   return (
     <ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
@@ -108,7 +119,7 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, desti
         {...dropzone.getRootProps()}
       >
         <input {...dropzone.getInputProps()} />
-        <AppContent />
+        <InvokeTabs />
         <AnimatePresence>
           {dropzone.isDragActive && isHandlingUpload && (
             <ImageUploadOverlay dropzone={dropzone} setIsHandlingUpload={setIsHandlingUpload} />
@@ -119,7 +130,6 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage, selectedWorkflowId, desti
       <ChangeBoardModal />
       <DynamicPromptsModal />
       <StylePresetModal />
-      <ClearQueueConfirmationsAlertDialog />
       <PreselectedImage selectedImage={selectedImage} />
     </ErrorBoundary>
   );

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

@@ -1,7 +1,5 @@
 import { Button, Flex, Heading, Image, Link, Text } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
-import { selectConfigSlice } from 'features/system/store/configSlice';
 import { toast } from 'features/toast/toast';
 import newGithubIssueUrl from 'new-github-issue-url';
 import InvokeLogoYellow from 'public/assets/images/invoke-symbol-ylw-lrg.svg';
@@ -15,11 +13,9 @@ type Props = {
   resetErrorBoundary: () => void;
 };
 
-const selectIsLocal = createSelector(selectConfigSlice, (config) => config.isLocal);
-
 const AppErrorBoundaryFallback = ({ error, resetErrorBoundary }: Props) => {
   const { t } = useTranslation();
-  const isLocal = useAppSelector(selectIsLocal);
+  const isLocal = useAppSelector((s) => s.config.isLocal);
 
   const handleCopy = useCallback(() => {
     const text = JSON.stringify(serializeError(error), null, 2);

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

@@ -19,7 +19,7 @@ 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 { TabName } from 'features/ui/store/uiTypes';
+import type { InvokeTabName } from 'features/ui/store/tabMap';
 import type { PropsWithChildren, ReactNode } from 'react';
 import React, { lazy, memo, useEffect, useMemo } from 'react';
 import { Provider } from 'react-redux';
@@ -45,7 +45,8 @@ interface Props extends PropsWithChildren {
     action: 'sendToImg2Img' | 'sendToCanvas' | 'useAllParameters';
   };
   selectedWorkflowId?: string;
-  destination?: TabName;
+  selectedStylePresetId?: string;
+  destination?: InvokeTabName;
   customStarUi?: CustomStarUi;
   socketOptions?: Partial<ManagerOptions & SocketOptions>;
   isDebugging?: boolean;
@@ -66,6 +67,7 @@ const InvokeAIUI = ({
   queueId,
   selectedImage,
   selectedWorkflowId,
+  selectedStylePresetId,
   destination,
   customStarUi,
   socketOptions,
@@ -227,6 +229,7 @@ const InvokeAIUI = ({
                 config={config}
                 selectedImage={selectedImage}
                 selectedWorkflowId={selectedWorkflowId}
+                selectedStylePresetId={selectedStylePresetId}
                 destination={destination}
               />
             </AppDndContext>

invokeai/frontend/web/src/app/hooks/useSocketIO.ts

@@ -2,7 +2,7 @@ import { useStore } from '@nanostores/react';
 import { $authToken } from 'app/store/nanostores/authToken';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
 import { $isDebugging } from 'app/store/nanostores/isDebugging';
-import { useAppStore } from 'app/store/nanostores/store';
+import { useAppDispatch } from 'app/store/storeHooks';
 import type { MapStore } from 'nanostores';
 import { atom, map } from 'nanostores';
 import { useEffect, useMemo } from 'react';
@@ -18,19 +18,14 @@ declare global {
   }
 }
 
-export type AppSocket = Socket<ServerToClientEvents, ClientToServerEvents>;
-
-export const $socket = atom<AppSocket | null>(null);
 export const $socketOptions = map<Partial<ManagerOptions & SocketOptions>>({});
-
 const $isSocketInitialized = atom<boolean>(false);
-export const $isConnected = atom<boolean>(false);
 
 /**
  * Initializes the socket.io connection and sets up event listeners.
  */
 export const useSocketIO = () => {
-  const { dispatch, getState } = useAppStore();
+  const dispatch = useAppDispatch();
   const baseUrl = useStore($baseUrl);
   const authToken = useStore($authToken);
   const addlSocketOptions = useStore($socketOptions);
@@ -66,9 +61,8 @@ export const useSocketIO = () => {
       return;
     }
 
-    const socket: AppSocket = io(socketUrl, socketOptions);
-    $socket.set(socket);
-    setEventListeners({ socket, dispatch, getState, setIsConnected: $isConnected.set });
+    const socket: Socket<ServerToClientEvents, ClientToServerEvents> = io(socketUrl, socketOptions);
+    setEventListeners({ dispatch, socket });
     socket.connect();
 
     if ($isDebugging.get() || import.meta.env.MODE === 'development') {
@@ -90,5 +84,5 @@ export const useSocketIO = () => {
       socket.disconnect();
       $isSocketInitialized.set(false);
     };
-  }, [dispatch, getState, socketOptions, socketUrl]);
+  }, [dispatch, socketOptions, socketUrl]);
 };

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

@@ -15,21 +15,21 @@ export const BASE_CONTEXT = {};
 
 export const $logger = atom<Logger>(Roarr.child(BASE_CONTEXT));
 
-export const zLogNamespace = z.enum([
-  'canvas',
-  'config',
-  'events',
-  'gallery',
-  'generation',
-  'metadata',
-  'models',
-  'system',
-  'queue',
-  'workflows',
-]);
-export type LogNamespace = z.infer<typeof zLogNamespace>;
+export type LoggerNamespace =
+  | 'images'
+  | 'models'
+  | 'config'
+  | 'canvas'
+  | 'generation'
+  | 'nodes'
+  | 'system'
+  | 'socketio'
+  | 'session'
+  | 'queue'
+  | 'dnd'
+  | 'controlLayers';
 
-export const logger = (namespace: LogNamespace) => $logger.get().child({ namespace });
+export const logger = (namespace: LoggerNamespace) => $logger.get().child({ namespace });
 
 export const zLogLevel = z.enum(['trace', 'debug', 'info', 'warn', 'error', 'fatal']);
 export type LogLevel = z.infer<typeof zLogLevel>;

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

@@ -1,41 +1,29 @@
 import { createLogWriter } from '@roarr/browser-log-writer';
 import { useAppSelector } from 'app/store/storeHooks';
-import {
-  selectSystemLogIsEnabled,
-  selectSystemLogLevel,
-  selectSystemLogNamespaces,
-} from 'features/system/store/systemSlice';
 import { useEffect, useMemo } from 'react';
 import { ROARR, Roarr } from 'roarr';
 
-import type { LogNamespace } from './logger';
+import type { LoggerNamespace } from './logger';
 import { $logger, BASE_CONTEXT, LOG_LEVEL_MAP, logger } from './logger';
 
-export const useLogger = (namespace: LogNamespace) => {
-  const logLevel = useAppSelector(selectSystemLogLevel);
-  const logNamespaces = useAppSelector(selectSystemLogNamespaces);
-  const logIsEnabled = useAppSelector(selectSystemLogIsEnabled);
+export const useLogger = (namespace: LoggerNamespace) => {
+  const consoleLogLevel = useAppSelector((s) => s.system.consoleLogLevel);
+  const shouldLogToConsole = useAppSelector((s) => s.system.shouldLogToConsole);
 
   // The provided Roarr browser log writer uses localStorage to config logging to console
   useEffect(() => {
-    if (logIsEnabled) {
+    if (shouldLogToConsole) {
       // Enable console log output
       localStorage.setItem('ROARR_LOG', 'true');
 
       // Use a filter to show only logs of the given level
-      let filter = `context.logLevel:>=${LOG_LEVEL_MAP[logLevel]}`;
-      if (logNamespaces.length > 0) {
-        filter += ` AND (${logNamespaces.map((ns) => `context.namespace:${ns}`).join(' OR ')})`;
-      } else {
-        filter += ' AND context.namespace:undefined';
-      }
-      localStorage.setItem('ROARR_FILTER', filter);
+      localStorage.setItem('ROARR_FILTER', `context.logLevel:>=${LOG_LEVEL_MAP[consoleLogLevel]}`);
     } else {
       // Disable console log output
       localStorage.setItem('ROARR_LOG', 'false');
     }
     ROARR.write = createLogWriter();
-  }, [logLevel, logIsEnabled, logNamespaces]);
+  }, [consoleLogLevel, shouldLogToConsole]);
 
   // Update the module-scoped logger context as needed
   useEffect(() => {

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

@@ -1,7 +1,7 @@
 import { createAction } from '@reduxjs/toolkit';
-import type { TabName } from 'features/ui/store/uiTypes';
+import type { InvokeTabName } from 'features/ui/store/tabMap';
 
 export const enqueueRequested = createAction<{
-  tabName: TabName;
+  tabName: InvokeTabName;
   prepend: boolean;
 }>('app/enqueueRequested');

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

@@ -1,3 +1,2 @@
 export const STORAGE_PREFIX = '@@invokeai-';
 export const EMPTY_ARRAY = [];
-export const EMPTY_OBJECT = {};

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

@@ -1,6 +1,5 @@
 import { createDraftSafeSelectorCreator, createSelectorCreator, lruMemoize } from '@reduxjs/toolkit';
 import type { GetSelectorsOptions } from '@reduxjs/toolkit/dist/entities/state_selectors';
-import type { RootState } from 'app/store/store';
 import { isEqual } from 'lodash-es';
 
 /**
@@ -20,5 +19,3 @@ export const getSelectorsOptions: GetSelectorsOptions = {
     argsMemoize: lruMemoize,
   }),
 };
-
-export const createMemoizedAppSelector = createMemoizedSelector.withTypes<RootState>();

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/errors.ts

@@ -1,4 +1,5 @@
 import { logger } from 'app/logging/logger';
+import { parseify } from 'common/util/serialize';
 import { PersistError, RehydrateError } from 'redux-remember';
 import { serializeError } from 'serialize-error';
 
@@ -40,6 +41,6 @@ export const errorHandler = (err: PersistError | RehydrateError) => {
   } else if (err instanceof RehydrateError) {
     log.error({ error: serializeError(err) }, 'Problem rehydrating state');
   } else {
-    log.error({ error: serializeError(err) }, 'Problem in persistence layer');
+    log.error({ error: parseify(err) }, 'Problem in persistence layer');
   }
 };

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -1,7 +1,9 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
+import { deepClone } from 'common/util/deepClone';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { Graph } from 'services/api/types';
+import { socketGeneratorProgress } from 'services/events/actions';
 
 export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
   if (isAnyGraphBuilt(action)) {
@@ -22,5 +24,13 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
     };
   }
 
+  if (socketGeneratorProgress.match(action)) {
+    const sanitized = deepClone(action);
+    if (sanitized.payload.data.progress_image) {
+      sanitized.payload.data.progress_image.dataURL = '<Progress image omitted>';
+    }
+    return sanitized;
+  }
+
   return action;
 };

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

@@ -1,7 +1,7 @@
 import type { TypedStartListening } from '@reduxjs/toolkit';
 import { createListenerMiddleware } from '@reduxjs/toolkit';
 import { addAdHocPostProcessingRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/addAdHocPostProcessingRequestedListener';
-import { addStagingListeners } from 'app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener';
+import { addCommitStagingAreaImageListener } from 'app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener';
 import { addAnyEnqueuedListener } from 'app/store/middleware/listenerMiddleware/listeners/anyEnqueued';
 import { addAppConfigReceivedListener } from 'app/store/middleware/listenerMiddleware/listeners/appConfigReceived';
 import { addAppStartedListener } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
@@ -9,6 +9,17 @@ import { addBatchEnqueuedListener } from 'app/store/middleware/listenerMiddlewar
 import { addDeleteBoardAndImagesFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted';
 import { addBoardIdSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/boardIdSelected';
 import { addBulkDownloadListeners } from 'app/store/middleware/listenerMiddleware/listeners/bulkDownload';
+import { addCanvasCopiedToClipboardListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasCopiedToClipboard';
+import { addCanvasDownloadedAsImageListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasDownloadedAsImage';
+import { addCanvasImageToControlNetListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasImageToControlNet';
+import { addCanvasMaskSavedToGalleryListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMaskSavedToGallery';
+import { addCanvasMaskToControlNetListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMaskToControlNet';
+import { addCanvasMergedListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasMerged';
+import { addCanvasSavedToGalleryListener } from 'app/store/middleware/listenerMiddleware/listeners/canvasSavedToGallery';
+import { addControlAdapterPreprocessor } from 'app/store/middleware/listenerMiddleware/listeners/controlAdapterPreprocessor';
+import { addControlNetAutoProcessListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetAutoProcess';
+import { addControlNetImageProcessedListener } from 'app/store/middleware/listenerMiddleware/listeners/controlNetImageProcessed';
+import { addEnqueueRequestedCanvasListener } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedCanvas';
 import { addEnqueueRequestedLinear } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear';
 import { addEnqueueRequestedNodes } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedNodes';
 import { addGalleryImageClickedListener } from 'app/store/middleware/listenerMiddleware/listeners/galleryImageClicked';
@@ -26,7 +37,16 @@ import { addModelSelectedListener } from 'app/store/middleware/listenerMiddlewar
 import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelsLoaded';
 import { addDynamicPromptsListener } from 'app/store/middleware/listenerMiddleware/listeners/promptChanged';
 import { addSetDefaultSettingsListener } from 'app/store/middleware/listenerMiddleware/listeners/setDefaultSettings';
-import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketConnected';
+import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketConnected';
+import { addSocketDisconnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketDisconnected';
+import { addGeneratorProgressEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketGeneratorProgress';
+import { addInvocationCompleteEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationComplete';
+import { addInvocationErrorEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationError';
+import { addInvocationStartedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketInvocationStarted';
+import { addModelInstallEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelInstall';
+import { addModelLoadEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketModelLoad';
+import { addSocketQueueItemStatusChangedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketio/socketQueueItemStatusChanged';
+import { addStagingAreaImageSavedListener } from 'app/store/middleware/listenerMiddleware/listeners/stagingAreaImageSaved';
 import { addUpdateAllNodesRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/updateAllNodesRequested';
 import { addWorkflowLoadRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/workflowLoadRequested';
 import type { AppDispatch, RootState } from 'app/store/store';
@@ -63,6 +83,7 @@ addGalleryImageClickedListener(startAppListening);
 addGalleryOffsetChangedListener(startAppListening);
 
 // User Invoked
+addEnqueueRequestedCanvasListener(startAppListening);
 addEnqueueRequestedNodes(startAppListening);
 addEnqueueRequestedLinear(startAppListening);
 addEnqueueRequestedUpscale(startAppListening);
@@ -70,23 +91,32 @@ addAnyEnqueuedListener(startAppListening);
 addBatchEnqueuedListener(startAppListening);
 
 // Canvas actions
-// addCanvasSavedToGalleryListener(startAppListening);
-// addCanvasMaskSavedToGalleryListener(startAppListening);
-// addCanvasImageToControlNetListener(startAppListening);
-// addCanvasMaskToControlNetListener(startAppListening);
-// addCanvasDownloadedAsImageListener(startAppListening);
-// addCanvasCopiedToClipboardListener(startAppListening);
-// addCanvasMergedListener(startAppListening);
-// addStagingAreaImageSavedListener(startAppListening);
-// addCommitStagingAreaImageListener(startAppListening);
-addStagingListeners(startAppListening);
+addCanvasSavedToGalleryListener(startAppListening);
+addCanvasMaskSavedToGalleryListener(startAppListening);
+addCanvasImageToControlNetListener(startAppListening);
+addCanvasMaskToControlNetListener(startAppListening);
+addCanvasDownloadedAsImageListener(startAppListening);
+addCanvasCopiedToClipboardListener(startAppListening);
+addCanvasMergedListener(startAppListening);
+addStagingAreaImageSavedListener(startAppListening);
+addCommitStagingAreaImageListener(startAppListening);
 
 // Socket.IO
+addGeneratorProgressEventListener(startAppListening);
+addInvocationCompleteEventListener(startAppListening);
+addInvocationErrorEventListener(startAppListening);
+addInvocationStartedEventListener(startAppListening);
 addSocketConnectedEventListener(startAppListening);
-
-// Gallery bulk download
+addSocketDisconnectedEventListener(startAppListening);
+addModelLoadEventListener(startAppListening);
+addModelInstallEventListener(startAppListening);
+addSocketQueueItemStatusChangedEventListener(startAppListening);
 addBulkDownloadListeners(startAppListening);
 
+// ControlNet
+addControlNetImageProcessedListener(startAppListening);
+addControlNetAutoProcessListener(startAppListening);
+
 // Boards
 addImageAddedToBoardFulfilledListener(startAppListening);
 addImageRemovedFromBoardFulfilledListener(startAppListening);
@@ -118,4 +148,4 @@ addAdHocPostProcessingRequestedListener(startAppListening);
 addDynamicPromptsListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
-// addControlAdapterPreprocessor(startAppListening);
+addControlAdapterPreprocessor(startAppListening);

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

@@ -1,21 +1,21 @@
 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 { parseify } from 'common/util/serialize';
 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';
 
-const log = logger('queue');
-
 export const adHocPostProcessingRequested = createAction<{ imageDTO: ImageDTO }>(`upscaling/postProcessingRequested`);
 
 export const addAdHocPostProcessingRequestedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: adHocPostProcessingRequested,
     effect: async (action, { dispatch, getState }) => {
+      const log = logger('session');
+
       const { imageDTO } = action.payload;
       const state = getState();
 
@@ -39,9 +39,9 @@ export const addAdHocPostProcessingRequestedListener = (startAppListening: AppSt
 
         const enqueueResult = await req.unwrap();
         req.reset();
-        log.debug({ enqueueResult } as SerializableObject, t('queue.graphQueued'));
+        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
       } catch (error) {
-        log.error({ enqueueBatchArg } as SerializableObject, t('queue.graphFailedToQueue'));
+        log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
 
         if (error instanceof Object && 'status' in error && error.status === 403) {
           return;

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

@@ -23,7 +23,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
    */
   startAppListening({
     matcher: matchAnyBoardDeleted,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const state = getState();
       const deletedBoardId = action.meta.arg.originalArgs;
       const { autoAddBoardId, selectedBoardId } = state.gallery;
@@ -44,7 +44,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
   // If we archived a board, it may end up hidden. If it's selected or the auto-add board, we should reset those.
   startAppListening({
     matcher: boardsApi.endpoints.updateBoard.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const state = getState();
       const { shouldShowArchivedBoards } = state.gallery;
 
@@ -61,7 +61,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
   // When we hide archived boards, if the selected or the auto-add board is archived, we should reset those.
   startAppListening({
     actionCreator: shouldShowArchivedBoardsChanged,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const shouldShowArchivedBoards = action.payload;
 
       // We only need to take action if we have just hidden archived boards.
@@ -100,7 +100,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
    */
   startAppListening({
     matcher: boardsApi.endpoints.listAllBoards.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const boards = action.payload;
       const state = getState();
       const { selectedBoardId, autoAddBoardId } = state.gallery;

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

@@ -1,37 +1,33 @@
+import { isAnyOf } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import {
-  sessionStagingAreaImageAccepted,
-  sessionStagingAreaReset,
-} from 'features/controlLayers/store/canvasSessionSlice';
-import { rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
-import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
-import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
-import { imageDTOToImageObject } from 'features/controlLayers/store/types';
+  canvasBatchIdsReset,
+  commitStagingAreaImage,
+  discardStagedImages,
+  resetCanvas,
+  setInitialCanvasImage,
+} from 'features/canvas/store/canvasSlice';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { queueApi } from 'services/api/endpoints/queue';
-import { $lastCanvasProgressEvent } from 'services/events/setEventListeners';
-import { assert } from 'tsafe';
 
-const log = logger('canvas');
+const matcher = isAnyOf(commitStagingAreaImage, discardStagedImages, resetCanvas, setInitialCanvasImage);
 
-export const addStagingListeners = (startAppListening: AppStartListening) => {
+export const addCommitStagingAreaImageListener = (startAppListening: AppStartListening) => {
   startAppListening({
-    actionCreator: sessionStagingAreaReset,
-    effect: async (_, { dispatch }) => {
+    matcher,
+    effect: async (_, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+      const { batchIds } = state.canvas;
+
       try {
         const req = dispatch(
-          queueApi.endpoints.cancelByBatchOrigin.initiate(
-            { origin: 'canvas' },
-            { fixedCacheKey: 'cancelByBatchOrigin' }
-          )
+          queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: batchIds }, { fixedCacheKey: 'cancelByBatchIds' })
         );
         const { canceled } = await req.unwrap();
         req.reset();
-
-        $lastCanvasProgressEvent.set(null);
-
         if (canceled > 0) {
           log.debug(`Canceled ${canceled} canvas batches`);
           toast({
@@ -40,6 +36,7 @@ export const addStagingListeners = (startAppListening: AppStartListening) => {
             status: 'success',
           });
         }
+        dispatch(canvasBatchIdsReset());
       } catch {
         log.error('Failed to cancel canvas batches');
         toast({
@@ -50,26 +47,4 @@ export const addStagingListeners = (startAppListening: AppStartListening) => {
       }
     },
   });
-
-  startAppListening({
-    actionCreator: sessionStagingAreaImageAccepted,
-    effect: (action, api) => {
-      const { index } = action.payload;
-      const state = api.getState();
-      const stagingAreaImage = state.canvasSession.stagedImages[index];
-
-      assert(stagingAreaImage, 'No staged image found to accept');
-      const { x, y } = selectCanvasSlice(state).bbox.rect;
-
-      const { imageDTO, offsetX, offsetY } = stagingAreaImage;
-      const imageObject = imageDTOToImageObject(imageDTO);
-      const overrides: Partial<CanvasRasterLayerState> = {
-        position: { x: x + offsetX, y: y + offsetY },
-        objects: [imageObject],
-      };
-
-      api.dispatch(rasterLayerAdded({ overrides, isSelected: true }));
-      api.dispatch(sessionStagingAreaReset());
-    },
-  });
 };

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

@@ -4,7 +4,7 @@ import { queueApi, selectQueueStatus } from 'services/api/endpoints/queue';
 export const addAnyEnqueuedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: queueApi.endpoints.enqueueBatch.matchFulfilled,
-    effect: (_, { dispatch, getState }) => {
+    effect: async (_, { dispatch, getState }) => {
       const { data } = selectQueueStatus(getState());
 
       if (!data || data.processor.is_started) {

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

@@ -1,14 +1,14 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { setInfillMethod } from 'features/controlLayers/store/paramsSlice';
+import { setInfillMethod } from 'features/parameters/store/generationSlice';
 import { shouldUseNSFWCheckerChanged, shouldUseWatermarkerChanged } from 'features/system/store/systemSlice';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 
 export const addAppConfigReceivedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: appInfoApi.endpoints.getAppConfig.matchFulfilled,
-    effect: (action, { getState, dispatch }) => {
+    effect: async (action, { getState, dispatch }) => {
       const { infill_methods = [], nsfw_methods = [], watermarking_methods = [] } = action.payload;
-      const infillMethod = getState().params.infillMethod;
+      const infillMethod = getState().generation.infillMethod;
 
       if (!infill_methods.includes(infillMethod)) {
         // if there is no infill method, set it to the first one

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

@@ -6,7 +6,7 @@ export const appStarted = createAction('app/appStarted');
 export const addAppStartedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: appStarted,
-    effect: (action, { unsubscribe, cancelActiveListeners }) => {
+    effect: async (action, { unsubscribe, cancelActiveListeners }) => {
       // this should only run once
       cancelActiveListeners();
       unsubscribe();

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

@@ -1,30 +1,27 @@
 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 { 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';
 
-const log = logger('queue');
-
 export const addBatchEnqueuedListener = (startAppListening: AppStartListening) => {
   // success
   startAppListening({
     matcher: queueApi.endpoints.enqueueBatch.matchFulfilled,
-    effect: (action) => {
-      const enqueueResult = action.payload;
+    effect: async (action) => {
+      const response = action.payload;
       const arg = action.meta.arg.originalArgs;
-      log.debug({ enqueueResult } as SerializableObject, 'Batch enqueued');
+      logger('queue').debug({ enqueueResult: parseify(response) }, 'Batch enqueued');
 
       toast({
         id: 'QUEUE_BATCH_SUCCEEDED',
         title: t('queue.batchQueued'),
         status: 'success',
         description: t('queue.batchQueuedDesc', {
-          count: enqueueResult.enqueued,
+          count: response.enqueued,
           direction: arg.prepend ? t('queue.front') : t('queue.back'),
         }),
       });
@@ -34,9 +31,9 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
   // error
   startAppListening({
     matcher: queueApi.endpoints.enqueueBatch.matchRejected,
-    effect: (action) => {
+    effect: async (action) => {
       const response = action.payload;
-      const batchConfig = action.meta.arg.originalArgs;
+      const arg = action.meta.arg.originalArgs;
 
       if (!response) {
         toast({
@@ -45,7 +42,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
           status: 'error',
           description: t('common.unknownError'),
         });
-        log.error({ batchConfig } as SerializableObject, t('queue.batchFailedToQueue'));
+        logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));
         return;
       }
 
@@ -71,7 +68,7 @@ export const addBatchEnqueuedListener = (startAppListening: AppStartListening) =
           description: t('common.unknownError'),
         });
       }
-      log.error({ batchConfig, error: serializeError(response) } as SerializableObject, t('queue.batchFailedToQueue'));
+      logger('queue').error({ batchConfig: parseify(arg), error: parseify(response) }, t('queue.batchFailedToQueue'));
     },
   });
 };

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

@@ -1,31 +1,47 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
+import { resetCanvas } from 'features/canvas/store/canvasSlice';
+import { controlAdaptersReset } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { allLayersDeleted } from 'features/controlLayers/store/controlLayersSlice';
 import { getImageUsage } from 'features/deleteImageModal/store/selectors';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
-import { selectNodesSlice } from 'features/nodes/store/selectors';
 import { imagesApi } from 'services/api/endpoints/images';
 
 export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.deleteBoardAndImages.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const { deleted_images } = action.payload;
 
       // Remove all deleted images from the UI
 
+      let wasCanvasReset = false;
       let wasNodeEditorReset = false;
+      let wereControlAdaptersReset = false;
+      let wereControlLayersReset = false;
 
-      const state = getState();
-      const nodes = selectNodesSlice(state);
-      const canvas = selectCanvasSlice(state);
-
+      const { canvas, nodes, controlAdapters, controlLayers } = getState();
       deleted_images.forEach((image_name) => {
-        const imageUsage = getImageUsage(nodes, canvas, image_name);
+        const imageUsage = getImageUsage(canvas, nodes.present, controlAdapters, controlLayers.present, image_name);
+
+        if (imageUsage.isCanvasImage && !wasCanvasReset) {
+          dispatch(resetCanvas());
+          wasCanvasReset = true;
+        }
 
         if (imageUsage.isNodesImage && !wasNodeEditorReset) {
           dispatch(nodeEditorReset());
           wasNodeEditorReset = true;
         }
+
+        if (imageUsage.isControlImage && !wereControlAdaptersReset) {
+          dispatch(controlAdaptersReset());
+          wereControlAdaptersReset = true;
+        }
+
+        if (imageUsage.isControlLayerImage && !wereControlLayersReset) {
+          dispatch(allLayersDeleted());
+          wereControlLayersReset = true;
+        }
       });
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/bulkDownload.tsx

@@ -1,15 +1,21 @@
+import { ExternalLink } from '@invoke-ai/ui-library';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';
+import {
+  socketBulkDownloadComplete,
+  socketBulkDownloadError,
+  socketBulkDownloadStarted,
+} from 'services/events/actions';
 
-const log = logger('gallery');
+const log = logger('images');
 
 export const addBulkDownloadListeners = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.bulkDownloadImages.matchFulfilled,
-    effect: (action) => {
+    effect: async (action) => {
       log.debug(action.payload, 'Bulk download requested');
 
       // If we have an item name, we are processing the bulk download locally and should use it as the toast id to
@@ -27,7 +33,7 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
 
   startAppListening({
     matcher: imagesApi.endpoints.bulkDownloadImages.matchRejected,
-    effect: () => {
+    effect: async () => {
       log.debug('Bulk download request failed');
 
       // There isn't any toast to update if we get this event.
@@ -38,4 +44,55 @@ export const addBulkDownloadListeners = (startAppListening: AppStartListening) =
       });
     },
   });
+
+  startAppListening({
+    actionCreator: socketBulkDownloadStarted,
+    effect: async (action) => {
+      // This should always happen immediately after the bulk download request, so we don't need to show a toast here.
+      log.debug(action.payload.data, 'Bulk download preparation started');
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketBulkDownloadComplete,
+    effect: async (action) => {
+      log.debug(action.payload.data, 'Bulk download preparation completed');
+
+      const { bulk_download_item_name } = action.payload.data;
+
+      // TODO(psyche): This URL may break in in some environments (e.g. Nvidia workbench) but we need to test it first
+      const url = `/api/v1/images/download/${bulk_download_item_name}`;
+
+      toast({
+        id: bulk_download_item_name,
+        title: t('gallery.bulkDownloadReady', 'Download ready'),
+        status: 'success',
+        description: (
+          <ExternalLink
+            label={t('gallery.clickToDownload', 'Click here to download')}
+            href={url}
+            download={bulk_download_item_name}
+          />
+        ),
+        duration: null,
+      });
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketBulkDownloadError,
+    effect: async (action) => {
+      log.debug(action.payload.data, 'Bulk download preparation failed');
+
+      const { bulk_download_item_name } = action.payload.data;
+
+      toast({
+        id: bulk_download_item_name,
+        title: t('gallery.bulkDownloadFailed'),
+        status: 'error',
+        description: action.payload.data.error,
+        duration: null,
+      });
+    },
+  });
 };

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

@@ -0,0 +1,38 @@
+import { $logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasCopiedToClipboard } from 'features/canvas/store/actions';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { copyBlobToClipboard } from 'features/system/util/copyBlobToClipboard';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+
+export const addCanvasCopiedToClipboardListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasCopiedToClipboard,
+    effect: async (action, { getState }) => {
+      const moduleLog = $logger.get().child({ namespace: 'canvasCopiedToClipboardListener' });
+      const state = getState();
+
+      try {
+        const blob = getBaseLayerBlob(state);
+
+        copyBlobToClipboard(blob);
+      } catch (err) {
+        moduleLog.error(String(err));
+        toast({
+          id: 'CANVAS_COPY_FAILED',
+          title: t('toast.problemCopyingCanvas'),
+          description: t('toast.problemCopyingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      toast({
+        id: 'CANVAS_COPY_SUCCEEDED',
+        title: t('toast.canvasCopiedClipboard'),
+        status: 'success',
+      });
+    },
+  });
+};

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

@@ -0,0 +1,34 @@
+import { $logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasDownloadedAsImage } from 'features/canvas/store/actions';
+import { downloadBlob } from 'features/canvas/util/downloadBlob';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+
+export const addCanvasDownloadedAsImageListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasDownloadedAsImage,
+    effect: async (action, { getState }) => {
+      const moduleLog = $logger.get().child({ namespace: 'canvasSavedToGalleryListener' });
+      const state = getState();
+
+      let blob;
+      try {
+        blob = await getBaseLayerBlob(state);
+      } catch (err) {
+        moduleLog.error(String(err));
+        toast({
+          id: 'CANVAS_DOWNLOAD_FAILED',
+          title: t('toast.problemDownloadingCanvas'),
+          description: t('toast.problemDownloadingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      downloadBlob(blob, 'canvas.png');
+      toast({ id: 'CANVAS_DOWNLOAD_SUCCEEDED', title: t('toast.canvasDownloaded'), status: 'success' });
+    },
+  });
+};

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

@@ -0,0 +1,60 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasImageToControlAdapter } from 'features/canvas/store/actions';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasImageToControlNetListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasImageToControlAdapter,
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+      const { id } = action.payload;
+
+      let blob: Blob;
+      try {
+        blob = await getBaseLayerBlob(state, true);
+      } catch (err) {
+        log.error(String(err));
+        toast({
+          id: 'PROBLEM_SAVING_CANVAS',
+          title: t('toast.problemSavingCanvas'),
+          description: t('toast.problemSavingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const { autoAddBoardId } = state.gallery;
+
+      const imageDTO = await dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([blob], 'savedCanvas.png', {
+            type: 'image/png',
+          }),
+          image_category: 'control',
+          is_intermediate: true,
+          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          crop_visible: false,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.canvasSentControlnetAssets'),
+          },
+        })
+      ).unwrap();
+
+      const { image_name } = imageDTO;
+
+      dispatch(
+        controlAdapterImageChanged({
+          id,
+          controlImage: image_name,
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,60 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasMaskSavedToGallery } from 'features/canvas/store/actions';
+import { getCanvasData } from 'features/canvas/util/getCanvasData';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasMaskSavedToGalleryListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasMaskSavedToGallery,
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+
+      const canvasBlobsAndImageData = await getCanvasData(
+        state.canvas.layerState,
+        state.canvas.boundingBoxCoordinates,
+        state.canvas.boundingBoxDimensions,
+        state.canvas.isMaskEnabled,
+        state.canvas.shouldPreserveMaskedArea
+      );
+
+      if (!canvasBlobsAndImageData) {
+        return;
+      }
+
+      const { maskBlob } = canvasBlobsAndImageData;
+
+      if (!maskBlob) {
+        log.error('Problem getting mask layer blob');
+        toast({
+          id: 'PROBLEM_SAVING_MASK',
+          title: t('toast.problemSavingMask'),
+          description: t('toast.problemSavingMaskDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const { autoAddBoardId } = state.gallery;
+
+      dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([maskBlob], 'canvasMaskImage.png', {
+            type: 'image/png',
+          }),
+          image_category: 'mask',
+          is_intermediate: false,
+          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          crop_visible: true,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.maskSavedAssets'),
+          },
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,70 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasMaskToControlAdapter } from 'features/canvas/store/actions';
+import { getCanvasData } from 'features/canvas/util/getCanvasData';
+import { controlAdapterImageChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasMaskToControlNetListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasMaskToControlAdapter,
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+      const { id } = action.payload;
+      const canvasBlobsAndImageData = await getCanvasData(
+        state.canvas.layerState,
+        state.canvas.boundingBoxCoordinates,
+        state.canvas.boundingBoxDimensions,
+        state.canvas.isMaskEnabled,
+        state.canvas.shouldPreserveMaskedArea
+      );
+
+      if (!canvasBlobsAndImageData) {
+        return;
+      }
+
+      const { maskBlob } = canvasBlobsAndImageData;
+
+      if (!maskBlob) {
+        log.error('Problem getting mask layer blob');
+        toast({
+          id: 'PROBLEM_IMPORTING_MASK',
+          title: t('toast.problemImportingMask'),
+          description: t('toast.problemImportingMaskDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const { autoAddBoardId } = state.gallery;
+
+      const imageDTO = await dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([maskBlob], 'canvasMaskImage.png', {
+            type: 'image/png',
+          }),
+          image_category: 'mask',
+          is_intermediate: true,
+          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          crop_visible: false,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.maskSentControlnetAssets'),
+          },
+        })
+      ).unwrap();
+
+      const { image_name } = imageDTO;
+
+      dispatch(
+        controlAdapterImageChanged({
+          id,
+          controlImage: image_name,
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,73 @@
+import { $logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { canvasMerged } from 'features/canvas/store/actions';
+import { $canvasBaseLayer } from 'features/canvas/store/canvasNanostore';
+import { setMergedCanvas } from 'features/canvas/store/canvasSlice';
+import { getFullBaseLayerBlob } from 'features/canvas/util/getFullBaseLayerBlob';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasMergedListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasMerged,
+    effect: async (action, { dispatch }) => {
+      const moduleLog = $logger.get().child({ namespace: 'canvasCopiedToClipboardListener' });
+      const blob = await getFullBaseLayerBlob();
+
+      if (!blob) {
+        moduleLog.error('Problem getting base layer blob');
+        toast({
+          id: 'PROBLEM_MERGING_CANVAS',
+          title: t('toast.problemMergingCanvas'),
+          description: t('toast.problemMergingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const canvasBaseLayer = $canvasBaseLayer.get();
+
+      if (!canvasBaseLayer) {
+        moduleLog.error('Problem getting canvas base layer');
+        toast({
+          id: 'PROBLEM_MERGING_CANVAS',
+          title: t('toast.problemMergingCanvas'),
+          description: t('toast.problemMergingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const baseLayerRect = canvasBaseLayer.getClientRect({
+        relativeTo: canvasBaseLayer.getParent() ?? undefined,
+      });
+
+      const imageDTO = await dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([blob], 'mergedCanvas.png', {
+            type: 'image/png',
+          }),
+          image_category: 'general',
+          is_intermediate: true,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.canvasMerged'),
+          },
+        })
+      ).unwrap();
+
+      // TODO: I can't figure out how to do the type narrowing in the `take()` so just brute forcing it here
+      const { image_name } = imageDTO;
+
+      dispatch(
+        setMergedCanvas({
+          kind: 'image',
+          layer: 'base',
+          imageName: image_name,
+          ...baseLayerRect,
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,53 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { parseify } from 'common/util/serialize';
+import { canvasSavedToGallery } from 'features/canvas/store/actions';
+import { getBaseLayerBlob } from 'features/canvas/util/getBaseLayerBlob';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addCanvasSavedToGalleryListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: canvasSavedToGallery,
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('canvas');
+      const state = getState();
+
+      let blob;
+      try {
+        blob = await getBaseLayerBlob(state);
+      } catch (err) {
+        log.error(String(err));
+        toast({
+          id: 'CANVAS_SAVE_FAILED',
+          title: t('toast.problemSavingCanvas'),
+          description: t('toast.problemSavingCanvasDesc'),
+          status: 'error',
+        });
+        return;
+      }
+
+      const { autoAddBoardId } = state.gallery;
+
+      dispatch(
+        imagesApi.endpoints.uploadImage.initiate({
+          file: new File([blob], 'savedCanvas.png', {
+            type: 'image/png',
+          }),
+          image_category: 'general',
+          is_intermediate: false,
+          board_id: autoAddBoardId === 'none' ? undefined : autoAddBoardId,
+          crop_visible: true,
+          postUploadAction: {
+            type: 'TOAST',
+            title: t('toast.canvasSavedGallery'),
+          },
+          metadata: {
+            _canvas_objects: parseify(state.canvas.layerState.objects),
+          },
+        })
+      );
+    },
+  });
+};

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

@@ -0,0 +1,194 @@
+import { isAnyOf } from '@reduxjs/toolkit';
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppDispatch } from 'app/store/store';
+import { parseify } from 'common/util/serialize';
+import {
+  caLayerImageChanged,
+  caLayerModelChanged,
+  caLayerProcessedImageChanged,
+  caLayerProcessorConfigChanged,
+  caLayerProcessorPendingBatchIdChanged,
+  caLayerRecalled,
+  isControlAdapterLayer,
+} from 'features/controlLayers/store/controlLayersSlice';
+import { CA_PROCESSOR_DATA } from 'features/controlLayers/util/controlAdapters';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { isEqual } from 'lodash-es';
+import { getImageDTO } from 'services/api/endpoints/images';
+import { queueApi } from 'services/api/endpoints/queue';
+import type { BatchConfig } from 'services/api/types';
+import { socketInvocationComplete } from 'services/events/actions';
+import { assert } from 'tsafe';
+
+const matcher = isAnyOf(
+  caLayerImageChanged,
+  caLayerProcessedImageChanged,
+  caLayerProcessorConfigChanged,
+  caLayerModelChanged,
+  caLayerRecalled
+);
+
+const DEBOUNCE_MS = 300;
+const log = logger('session');
+
+/**
+ * Simple helper to cancel a batch and reset the pending batch ID
+ */
+const cancelProcessorBatch = async (dispatch: AppDispatch, layerId: string, batchId: string) => {
+  const req = dispatch(queueApi.endpoints.cancelByBatchIds.initiate({ batch_ids: [batchId] }));
+  log.trace({ batchId }, 'Cancelling existing preprocessor batch');
+  try {
+    await req.unwrap();
+  } catch {
+    // no-op
+  } finally {
+    req.reset();
+    // Always reset the pending batch ID - the cancel req could fail if the batch doesn't exist
+    dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
+  }
+};
+
+export const addControlAdapterPreprocessor = (startAppListening: AppStartListening) => {
+  startAppListening({
+    matcher,
+    effect: async (action, { dispatch, getState, getOriginalState, cancelActiveListeners, delay, take, signal }) => {
+      const layerId = caLayerRecalled.match(action) ? action.payload.id : action.payload.layerId;
+      const state = getState();
+      const originalState = getOriginalState();
+
+      // Cancel any in-progress instances of this listener
+      cancelActiveListeners();
+      log.trace('Control Layer CA auto-process triggered');
+
+      // Delay before starting actual work
+      await delay(DEBOUNCE_MS);
+
+      const layer = state.controlLayers.present.layers.filter(isControlAdapterLayer).find((l) => l.id === layerId);
+
+      if (!layer) {
+        return;
+      }
+
+      // We should only process if the processor settings or image have changed
+      const originalLayer = originalState.controlLayers.present.layers
+        .filter(isControlAdapterLayer)
+        .find((l) => l.id === layerId);
+      const originalImage = originalLayer?.controlAdapter.image;
+      const originalConfig = originalLayer?.controlAdapter.processorConfig;
+
+      const image = layer.controlAdapter.image;
+      const processedImage = layer.controlAdapter.processedImage;
+      const config = layer.controlAdapter.processorConfig;
+
+      if (isEqual(config, originalConfig) && isEqual(image, originalImage) && processedImage) {
+        // Neither config nor image have changed, we can bail
+        return;
+      }
+
+      if (!image || !config) {
+        // - If we have no image, we have nothing to process
+        // - If we have no processor config, we have nothing to process
+        // Clear the processed image and bail
+        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO: null }));
+        return;
+      }
+
+      // At this point, the user has stopped fiddling with the processor settings and there is a processor selected.
+
+      // If there is a pending processor batch, cancel it.
+      if (layer.controlAdapter.processorPendingBatchId) {
+        cancelProcessorBatch(dispatch, layerId, layer.controlAdapter.processorPendingBatchId);
+      }
+
+      // TODO(psyche): I can't get TS to be happy, it thinkgs `config` is `never` but it should be inferred from the generic... I'll just cast it for now
+      const processorNode = CA_PROCESSOR_DATA[config.type].buildNode(image, config as never);
+      const enqueueBatchArg: BatchConfig = {
+        prepend: true,
+        batch: {
+          graph: {
+            nodes: {
+              [processorNode.id]: {
+                ...processorNode,
+                // Control images are always intermediate - do not save to gallery
+                is_intermediate: true,
+              },
+            },
+            edges: [],
+          },
+          runs: 1,
+        },
+      };
+
+      // Kick off the processor batch
+      const req = dispatch(
+        queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
+          fixedCacheKey: 'enqueueBatch',
+        })
+      );
+
+      try {
+        const enqueueResult = await req.unwrap();
+        // TODO(psyche): Update the pydantic models, pretty sure we will _always_ have a batch_id here, but the model says it's optional
+        assert(enqueueResult.batch.batch_id, 'Batch ID not returned from queue');
+        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: enqueueResult.batch.batch_id }));
+        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
+
+        // Wait for the processor node to complete
+        const [invocationCompleteAction] = await take(
+          (action): action is ReturnType<typeof socketInvocationComplete> =>
+            socketInvocationComplete.match(action) &&
+            action.payload.data.batch_id === enqueueResult.batch.batch_id &&
+            action.payload.data.invocation_source_id === processorNode.id
+        );
+
+        // We still have to check the output type
+        assert(
+          invocationCompleteAction.payload.data.result.type === 'image_output',
+          `Processor did not return an image output, got: ${invocationCompleteAction.payload.data.result}`
+        );
+        const { image_name } = invocationCompleteAction.payload.data.result.image;
+
+        const imageDTO = await getImageDTO(image_name);
+        assert(imageDTO, "Failed to fetch processor output's image DTO");
+
+        // Whew! We made it. Update the layer with the processed image
+        log.debug({ layerId, imageDTO }, 'ControlNet image processed');
+        dispatch(caLayerProcessedImageChanged({ layerId, imageDTO }));
+        dispatch(caLayerProcessorPendingBatchIdChanged({ layerId, batchId: null }));
+      } catch (error) {
+        if (signal.aborted) {
+          // The listener was canceled - we need to cancel the pending processor batch, if there is one (could have changed by now).
+          const pendingBatchId = getState()
+            .controlLayers.present.layers.filter(isControlAdapterLayer)
+            .find((l) => l.id === layerId)?.controlAdapter.processorPendingBatchId;
+          if (pendingBatchId) {
+            cancelProcessorBatch(dispatch, layerId, pendingBatchId);
+          }
+          log.trace('Control Adapter preprocessor cancelled');
+        } else {
+          // Some other error condition...
+          log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
+
+          if (error instanceof Object) {
+            if ('data' in error && 'status' in error) {
+              if (error.status === 403) {
+                dispatch(caLayerImageChanged({ layerId, imageDTO: null }));
+                return;
+              }
+            }
+          }
+
+          toast({
+            id: 'GRAPH_QUEUE_FAILED',
+            title: t('queue.graphFailedToQueue'),
+            status: 'error',
+          });
+        }
+      } finally {
+        req.reset();
+      }
+    },
+  });
+};

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

@@ -0,0 +1,85 @@
+import type { AnyListenerPredicate } from '@reduxjs/toolkit';
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { RootState } from 'app/store/store';
+import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions';
+import {
+  controlAdapterAutoConfigToggled,
+  controlAdapterImageChanged,
+  controlAdapterModelChanged,
+  controlAdapterProcessorParamsChanged,
+  controlAdapterProcessortTypeChanged,
+  selectControlAdapterById,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
+
+type AnyControlAdapterParamChangeAction =
+  | ReturnType<typeof controlAdapterProcessorParamsChanged>
+  | ReturnType<typeof controlAdapterModelChanged>
+  | ReturnType<typeof controlAdapterImageChanged>
+  | ReturnType<typeof controlAdapterProcessortTypeChanged>
+  | ReturnType<typeof controlAdapterAutoConfigToggled>;
+
+const predicate: AnyListenerPredicate<RootState> = (action, state, prevState) => {
+  const isActionMatched =
+    controlAdapterProcessorParamsChanged.match(action) ||
+    controlAdapterModelChanged.match(action) ||
+    controlAdapterImageChanged.match(action) ||
+    controlAdapterProcessortTypeChanged.match(action) ||
+    controlAdapterAutoConfigToggled.match(action);
+
+  if (!isActionMatched) {
+    return false;
+  }
+
+  const { id } = action.payload;
+  const prevCA = selectControlAdapterById(prevState.controlAdapters, id);
+  const ca = selectControlAdapterById(state.controlAdapters, id);
+  if (!prevCA || !isControlNetOrT2IAdapter(prevCA) || !ca || !isControlNetOrT2IAdapter(ca)) {
+    return false;
+  }
+
+  if (controlAdapterAutoConfigToggled.match(action)) {
+    // do not process if the user just disabled auto-config
+    if (prevCA.shouldAutoConfig === true) {
+      return false;
+    }
+  }
+
+  const { controlImage, processorType, shouldAutoConfig } = ca;
+  if (controlAdapterModelChanged.match(action) && !shouldAutoConfig) {
+    // do not process if the action is a model change but the processor settings are dirty
+    return false;
+  }
+
+  const isProcessorSelected = processorType !== 'none';
+
+  const hasControlImage = Boolean(controlImage);
+
+  return isProcessorSelected && hasControlImage;
+};
+
+const DEBOUNCE_MS = 300;
+
+/**
+ * Listener that automatically processes a ControlNet image when its processor parameters are changed.
+ *
+ * The network request is debounced.
+ */
+export const addControlNetAutoProcessListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    predicate,
+    effect: async (action, { dispatch, cancelActiveListeners, delay }) => {
+      const log = logger('session');
+      const { id } = (action as AnyControlAdapterParamChangeAction).payload;
+
+      // Cancel any in-progress instances of this listener
+      cancelActiveListeners();
+      log.trace('ControlNet auto-process triggered');
+      // Delay before starting actual work
+      await delay(DEBOUNCE_MS);
+
+      dispatch(controlAdapterImageProcessed({ id }));
+    },
+  });
+};

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

@@ -0,0 +1,118 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { parseify } from 'common/util/serialize';
+import { controlAdapterImageProcessed } from 'features/controlAdapters/store/actions';
+import {
+  controlAdapterImageChanged,
+  controlAdapterProcessedImageChanged,
+  pendingControlImagesCleared,
+  selectControlAdapterById,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+import { queueApi } from 'services/api/endpoints/queue';
+import type { BatchConfig, ImageDTO } from 'services/api/types';
+import { socketInvocationComplete } from 'services/events/actions';
+
+export const addControlNetImageProcessedListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: controlAdapterImageProcessed,
+    effect: async (action, { dispatch, getState, take }) => {
+      const log = logger('session');
+      const { id } = action.payload;
+      const ca = selectControlAdapterById(getState().controlAdapters, id);
+
+      if (!ca?.controlImage || !isControlNetOrT2IAdapter(ca)) {
+        log.error('Unable to process ControlNet image');
+        return;
+      }
+
+      if (ca.processorType === 'none' || ca.processorNode.type === 'none') {
+        return;
+      }
+
+      // ControlNet one-off procressing graph is just the processor node, no edges.
+      // Also we need to grab the image.
+
+      const nodeId = ca.processorNode.id;
+      const enqueueBatchArg: BatchConfig = {
+        prepend: true,
+        batch: {
+          graph: {
+            nodes: {
+              [ca.processorNode.id]: {
+                ...ca.processorNode,
+                is_intermediate: true,
+                use_cache: false,
+                image: { image_name: ca.controlImage },
+              },
+            },
+            edges: [],
+          },
+          runs: 1,
+        },
+      };
+
+      try {
+        const req = dispatch(
+          queueApi.endpoints.enqueueBatch.initiate(enqueueBatchArg, {
+            fixedCacheKey: 'enqueueBatch',
+          })
+        );
+        const enqueueResult = await req.unwrap();
+        req.reset();
+        log.debug({ enqueueResult: parseify(enqueueResult) }, t('queue.graphQueued'));
+
+        const [invocationCompleteAction] = await take(
+          (action): action is ReturnType<typeof socketInvocationComplete> =>
+            socketInvocationComplete.match(action) &&
+            action.payload.data.batch_id === enqueueResult.batch.batch_id &&
+            action.payload.data.invocation_source_id === nodeId
+        );
+
+        // We still have to check the output type
+        if (invocationCompleteAction.payload.data.result.type === 'image_output') {
+          const { image_name } = invocationCompleteAction.payload.data.result.image;
+
+          // Wait for the ImageDTO to be received
+          const [{ payload }] = await take(
+            (action) =>
+              imagesApi.endpoints.getImageDTO.matchFulfilled(action) && action.payload.image_name === image_name
+          );
+
+          const processedControlImage = payload as ImageDTO;
+
+          log.debug({ controlNetId: action.payload, processedControlImage }, 'ControlNet image processed');
+
+          // Update the processed image in the store
+          dispatch(
+            controlAdapterProcessedImageChanged({
+              id,
+              processedControlImage: processedControlImage.image_name,
+            })
+          );
+        }
+      } catch (error) {
+        log.error({ enqueueBatchArg: parseify(enqueueBatchArg) }, t('queue.graphFailedToQueue'));
+
+        if (error instanceof Object) {
+          if ('data' in error && 'status' in error) {
+            if (error.status === 403) {
+              dispatch(pendingControlImagesCleared());
+              dispatch(controlAdapterImageChanged({ id, controlImage: null }));
+              return;
+            }
+          }
+        }
+
+        toast({
+          id: 'GRAPH_QUEUE_FAILED',
+          title: t('queue.graphFailedToQueue'),
+          status: 'error',
+        });
+      }
+    },
+  });
+};

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

@@ -0,0 +1,144 @@
+import { logger } from 'app/logging/logger';
+import { enqueueRequested } from 'app/store/actions';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import openBase64ImageInTab from 'common/util/openBase64ImageInTab';
+import { parseify } from 'common/util/serialize';
+import { canvasBatchIdAdded, stagingAreaInitialized } from 'features/canvas/store/canvasSlice';
+import { blobToDataURL } from 'features/canvas/util/blobToDataURL';
+import { getCanvasData } from 'features/canvas/util/getCanvasData';
+import { getCanvasGenerationMode } from 'features/canvas/util/getCanvasGenerationMode';
+import { canvasGraphBuilt } from 'features/nodes/store/actions';
+import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
+import { buildCanvasGraph } from 'features/nodes/util/graph/canvas/buildCanvasGraph';
+import { imagesApi } from 'services/api/endpoints/images';
+import { queueApi } from 'services/api/endpoints/queue';
+import type { ImageDTO } from 'services/api/types';
+
+/**
+ * This listener is responsible invoking the canvas. This involves a number of steps:
+ *
+ * 1. Generate image blobs from the canvas layers
+ * 2. Determine the generation mode from the layers (txt2img, img2img, inpaint)
+ * 3. Build the canvas graph
+ * 4. Create the session with the graph
+ * 5. Upload the init image if necessary
+ * 6. Upload the mask image if necessary
+ * 7. Update the init and mask images with the session ID
+ * 8. Initialize the staging area if not yet initialized
+ * 9. Dispatch the sessionReadyToInvoke action to invoke the session
+ */
+export const addEnqueueRequestedCanvasListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    predicate: (action): action is ReturnType<typeof enqueueRequested> =>
+      enqueueRequested.match(action) && action.payload.tabName === 'canvas',
+    effect: async (action, { getState, dispatch }) => {
+      const log = logger('queue');
+      const { prepend } = action.payload;
+      const state = getState();
+
+      const { layerState, boundingBoxCoordinates, boundingBoxDimensions, isMaskEnabled, shouldPreserveMaskedArea } =
+        state.canvas;
+
+      // Build canvas blobs
+      const canvasBlobsAndImageData = await getCanvasData(
+        layerState,
+        boundingBoxCoordinates,
+        boundingBoxDimensions,
+        isMaskEnabled,
+        shouldPreserveMaskedArea
+      );
+
+      if (!canvasBlobsAndImageData) {
+        log.error('Unable to create canvas data');
+        return;
+      }
+
+      const { baseBlob, baseImageData, maskBlob, maskImageData } = canvasBlobsAndImageData;
+
+      // Determine the generation mode
+      const generationMode = getCanvasGenerationMode(baseImageData, maskImageData);
+
+      if (state.system.enableImageDebugging) {
+        const baseDataURL = await blobToDataURL(baseBlob);
+        const maskDataURL = await blobToDataURL(maskBlob);
+        openBase64ImageInTab([
+          { base64: maskDataURL, caption: 'mask b64' },
+          { base64: baseDataURL, caption: 'image b64' },
+        ]);
+      }
+
+      log.debug(`Generation mode: ${generationMode}`);
+
+      // Temp placeholders for the init and mask images
+      let canvasInitImage: ImageDTO | undefined;
+      let canvasMaskImage: ImageDTO | undefined;
+
+      // For img2img and inpaint/outpaint, we need to upload the init images
+      if (['img2img', 'inpaint', 'outpaint'].includes(generationMode)) {
+        // upload the image, saving the request id
+        canvasInitImage = await dispatch(
+          imagesApi.endpoints.uploadImage.initiate({
+            file: new File([baseBlob], 'canvasInitImage.png', {
+              type: 'image/png',
+            }),
+            image_category: 'general',
+            is_intermediate: true,
+          })
+        ).unwrap();
+      }
+
+      // For inpaint/outpaint, we also need to upload the mask layer
+      if (['inpaint', 'outpaint'].includes(generationMode)) {
+        // upload the image, saving the request id
+        canvasMaskImage = await dispatch(
+          imagesApi.endpoints.uploadImage.initiate({
+            file: new File([maskBlob], 'canvasMaskImage.png', {
+              type: 'image/png',
+            }),
+            image_category: 'mask',
+            is_intermediate: true,
+          })
+        ).unwrap();
+      }
+
+      const graph = await buildCanvasGraph(state, generationMode, canvasInitImage, canvasMaskImage);
+
+      log.debug({ graph: parseify(graph) }, `Canvas graph built`);
+
+      // currently this action is just listened to for logging
+      dispatch(canvasGraphBuilt(graph));
+
+      const batchConfig = prepareLinearUIBatch(state, graph, prepend);
+
+      try {
+        const req = dispatch(
+          queueApi.endpoints.enqueueBatch.initiate(batchConfig, {
+            fixedCacheKey: 'enqueueBatch',
+          })
+        );
+
+        const enqueueResult = await req.unwrap();
+        req.reset();
+
+        const batchId = enqueueResult.batch.batch_id as string; // we know the is a string, backend provides it
+
+        // Prep the canvas staging area if it is not yet initialized
+        if (!state.canvas.layerState.stagingArea.boundingBox) {
+          dispatch(
+            stagingAreaInitialized({
+              boundingBox: {
+                ...state.canvas.boundingBoxCoordinates,
+                ...state.canvas.boundingBoxDimensions,
+              },
+            })
+          );
+        }
+
+        // Associate the session with the canvas session ID
+        dispatch(canvasBatchIdAdded(batchId));
+      } catch {
+        // no-op
+      }
+    },
+  });
+};

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

@@ -1,21 +1,10 @@
-import { logger } from 'app/logging/logger';
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { SerializableObject } from 'common/types';
-import type { Result } from 'common/util/result';
-import { isErr, withResult, withResultAsync } from 'common/util/result';
-import { $canvasManager } from 'features/controlLayers/konva/CanvasManager';
-import { sessionStagingAreaReset, sessionStartedStaging } from 'features/controlLayers/store/canvasSessionSlice';
+import { isImageViewerOpenChanged } from 'features/gallery/store/gallerySlice';
 import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
-import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
-import type { Graph } from 'features/nodes/util/graph/generation/Graph';
-import { serializeError } from 'serialize-error';
+import { buildGenerationTabGraph } from 'features/nodes/util/graph/generation/buildGenerationTabGraph';
+import { buildGenerationTabSDXLGraph } from 'features/nodes/util/graph/generation/buildGenerationTabSDXLGraph';
 import { queueApi } from 'services/api/endpoints/queue';
-import type { Invocation } from 'services/api/types';
-import { assert } from 'tsafe';
-
-const log = logger('generation');
 
 export const addEnqueueRequestedLinear = (startAppListening: AppStartListening) => {
   startAppListening({
@@ -23,77 +12,33 @@ export const addEnqueueRequestedLinear = (startAppListening: AppStartListening)
       enqueueRequested.match(action) && action.payload.tabName === 'generation',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const model = state.params.model;
+      const { shouldShowProgressInViewer } = state.ui;
+      const model = state.generation.model;
       const { prepend } = action.payload;
 
-      const manager = $canvasManager.get();
-      assert(manager, 'No model found in state');
+      let graph;
 
-      let didStartStaging = false;
-
-      if (!state.canvasSession.isStaging && state.canvasSession.mode === 'compose') {
-        dispatch(sessionStartedStaging());
-        didStartStaging = true;
+      if (model?.base === 'sdxl') {
+        graph = await buildGenerationTabSDXLGraph(state);
+      } else {
+        graph = await buildGenerationTabGraph(state);
       }
 
-      const abortStaging = () => {
-        if (didStartStaging && getState().canvasSession.isStaging) {
-          dispatch(sessionStagingAreaReset());
-        }
-      };
-
-      let buildGraphResult: Result<
-        { g: Graph; noise: Invocation<'noise'>; posCond: Invocation<'compel' | 'sdxl_compel_prompt'> },
-        Error
-      >;
-
-      assert(model, 'No model found in state');
-      const base = model.base;
-
-      switch (base) {
-        case 'sdxl':
-          buildGraphResult = await withResultAsync(() => buildSDXLGraph(state, manager));
-          break;
-        case 'sd-1':
-        case `sd-2`:
-          buildGraphResult = await withResultAsync(() => buildSD1Graph(state, manager));
-          break;
-        default:
-          assert(false, `No graph builders for base ${base}`);
-      }
-
-      if (isErr(buildGraphResult)) {
-        log.error({ error: serializeError(buildGraphResult.error) }, 'Failed to build graph');
-        abortStaging();
-        return;
-      }
-
-      const { g, noise, posCond } = buildGraphResult.value;
-
-      const prepareBatchResult = withResult(() => prepareLinearUIBatch(state, g, prepend, noise, posCond));
-
-      if (isErr(prepareBatchResult)) {
-        log.error({ error: serializeError(prepareBatchResult.error) }, 'Failed to prepare batch');
-        abortStaging();
-        return;
-      }
+      const batchConfig = prepareLinearUIBatch(state, graph, prepend);
 
       const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(prepareBatchResult.value, {
+        queueApi.endpoints.enqueueBatch.initiate(batchConfig, {
           fixedCacheKey: 'enqueueBatch',
         })
       );
-      req.reset();
-
-      const enqueueResult = await withResultAsync(() => req.unwrap());
-
-      if (isErr(enqueueResult)) {
-        log.error({ error: serializeError(enqueueResult.error) }, 'Failed to enqueue batch');
-        abortStaging();
-        return;
+      try {
+        await req.unwrap();
+        if (shouldShowProgressInViewer) {
+          dispatch(isImageViewerOpenChanged(true));
+        }
+      } finally {
+        req.reset();
       }
-
-      log.debug({ batchConfig: prepareBatchResult.value } as SerializableObject, 'Enqueued batch');
     },
   });
 };

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

@@ -1,6 +1,5 @@
 import { enqueueRequested } from 'app/store/actions';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { selectNodesSlice } from 'features/nodes/store/selectors';
 import { buildNodesGraph } from 'features/nodes/util/graph/buildNodesGraph';
 import { buildWorkflowWithValidation } from 'features/nodes/util/workflow/buildWorkflow';
 import { queueApi } from 'services/api/endpoints/queue';
@@ -12,12 +11,12 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
       enqueueRequested.match(action) && action.payload.tabName === 'workflows',
     effect: async (action, { getState, dispatch }) => {
       const state = getState();
-      const nodes = selectNodesSlice(state);
+      const { nodes, edges } = state.nodes.present;
       const workflow = state.workflow;
-      const graph = buildNodesGraph(nodes);
+      const graph = buildNodesGraph(state.nodes.present);
       const builtWorkflow = buildWorkflowWithValidation({
-        nodes: nodes.nodes,
-        edges: nodes.edges,
+        nodes,
+        edges,
         workflow,
       });
 
@@ -30,8 +29,7 @@ export const addEnqueueRequestedNodes = (startAppListening: AppStartListening) =
         batch: {
           graph,
           workflow: builtWorkflow,
-          runs: state.params.iterations,
-          origin: 'workflows',
+          runs: state.generation.iterations,
         },
         prepend: action.payload.prepend,
       };

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

@@ -14,9 +14,9 @@ export const addEnqueueRequestedUpscale = (startAppListening: AppStartListening)
       const { shouldShowProgressInViewer } = state.ui;
       const { prepend } = action.payload;
 
-      const { g, noise, posCond } = await buildMultidiffusionUpscaleGraph(state);
+      const graph = await buildMultidiffusionUpscaleGraph(state);
 
-      const batchConfig = prepareLinearUIBatch(state, g, prepend, noise, posCond);
+      const batchConfig = prepareLinearUIBatch(state, graph, prepend);
 
       const req = dispatch(
         queueApi.endpoints.enqueueBatch.initiate(batchConfig, {

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

@@ -27,7 +27,7 @@ export const galleryImageClicked = createAction<{
 export const addGalleryImageClickedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: galleryImageClicked,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const { imageDTO, shiftKey, ctrlKey, metaKey, altKey } = action.payload;
       const state = getState();
       const queryArgs = selectListImagesQueryArgs(state);

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

@@ -1,27 +1,24 @@
 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';
 
-const log = logger('system');
-
 export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
     effect: (action, { getState }) => {
+      const log = logger('system');
       const schemaJSON = action.payload;
 
-      log.debug({ schemaJSON: parseify(schemaJSON) } as SerializableObject, 'Received OpenAPI schema');
+      log.debug({ schemaJSON: parseify(schemaJSON) }, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
 
-      log.debug({ nodeTemplates } as SerializableObject, `Built ${size(nodeTemplates)} node templates`);
+      log.debug({ nodeTemplates: parseify(nodeTemplates) }, `Built ${size(nodeTemplates)} node templates`);
 
       $templates.set(nodeTemplates);
     },
@@ -33,7 +30,8 @@ export const addGetOpenAPISchemaListener = (startAppListening: AppStartListening
       // If action.meta.condition === true, the request was canceled/skipped because another request was in flight or
       // the value was already in the cache. We don't want to log these errors.
       if (!action.meta.condition) {
-        log.error({ error: serializeError(action.error) }, 'Problem retrieving OpenAPI Schema');
+        const log = logger('system');
+        log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
       }
     },
   });

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

@@ -2,13 +2,15 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { imagesApi } from 'services/api/endpoints/images';
 
-const log = logger('gallery');
-
 export const addImageAddedToBoardFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.addImageToBoard.matchFulfilled,
     effect: (action) => {
+      const log = logger('images');
       const { board_id, imageDTO } = action.meta.arg.originalArgs;
+
+      // TODO: update listImages cache for this board
+
       log.debug({ board_id, imageDTO }, 'Image added to board');
     },
   });
@@ -16,7 +18,9 @@ export const addImageAddedToBoardFulfilledListener = (startAppListening: AppStar
   startAppListening({
     matcher: imagesApi.endpoints.addImageToBoard.matchRejected,
     effect: (action) => {
+      const log = logger('images');
       const { board_id, imageDTO } = action.meta.arg.originalArgs;
+
       log.debug({ board_id, imageDTO }, 'Problem adding image to board');
     },
   });

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

@@ -1,9 +1,20 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import type { AppDispatch, RootState } from 'app/store/store';
-import { entityDeleted, ipaImageChanged } from 'features/controlLayers/store/canvasSlice';
-import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
-import { getEntityIdentifier } from 'features/controlLayers/store/types';
+import { resetCanvas } from 'features/canvas/store/canvasSlice';
+import {
+  controlAdapterImageChanged,
+  controlAdapterProcessedImageChanged,
+  selectControlAdapterAll,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { isControlNetOrT2IAdapter } from 'features/controlAdapters/store/types';
+import {
+  isControlAdapterLayer,
+  isInitialImageLayer,
+  isIPAdapterLayer,
+  isRegionalGuidanceLayer,
+  layerDeleted,
+} from 'features/controlLayers/store/controlLayersSlice';
 import { imageDeletionConfirmed } from 'features/deleteImageModal/store/actions';
 import { isModalOpenChanged } from 'features/deleteImageModal/store/slice';
 import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
@@ -15,10 +26,6 @@ import { forEach, intersectionBy } from 'lodash-es';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 
-const log = logger('gallery');
-
-//TODO(psyche): handle image deletion (canvas sessions?)
-
 // Some utils to delete images from different parts of the app
 const deleteNodesImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
   state.nodes.present.nodes.forEach((node) => {
@@ -40,37 +47,52 @@ const deleteNodesImages = (state: RootState, dispatch: AppDispatch, imageDTO: Im
   });
 };
 
-// const deleteControlAdapterImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-//   state.canvas.present.controlAdapters.entities.forEach(({ id, imageObject, processedImageObject }) => {
-//     if (
-//       imageObject?.image.image_name === imageDTO.image_name ||
-//       processedImageObject?.image.image_name === imageDTO.image_name
-//     ) {
-//       dispatch(caImageChanged({ id, imageDTO: null }));
-//       dispatch(caProcessedImageChanged({ id, imageDTO: null }));
-//     }
-//   });
-// };
-
-const deleteIPAdapterImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  selectCanvasSlice(state).ipAdapters.entities.forEach((entity) => {
-    if (entity.ipAdapter.image?.image_name === imageDTO.image_name) {
-      dispatch(ipaImageChanged({ entityIdentifier: getEntityIdentifier(entity), imageDTO: null }));
+const deleteControlAdapterImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
+  forEach(selectControlAdapterAll(state.controlAdapters), (ca) => {
+    if (
+      ca.controlImage === imageDTO.image_name ||
+      (isControlNetOrT2IAdapter(ca) && ca.processedControlImage === imageDTO.image_name)
+    ) {
+      dispatch(
+        controlAdapterImageChanged({
+          id: ca.id,
+          controlImage: null,
+        })
+      );
+      dispatch(
+        controlAdapterProcessedImageChanged({
+          id: ca.id,
+          processedControlImage: null,
+        })
+      );
     }
   });
 };
 
-const deleteLayerImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
-  selectCanvasSlice(state).rasterLayers.entities.forEach(({ id, objects }) => {
-    let shouldDelete = false;
-    for (const obj of objects) {
-      if (obj.type === 'image' && obj.image.image_name === imageDTO.image_name) {
-        shouldDelete = true;
-        break;
+const deleteControlLayerImages = (state: RootState, dispatch: AppDispatch, imageDTO: ImageDTO) => {
+  state.controlLayers.present.layers.forEach((l) => {
+    if (isRegionalGuidanceLayer(l)) {
+      if (l.ipAdapters.some((ipa) => ipa.image?.name === imageDTO.image_name)) {
+        dispatch(layerDeleted(l.id));
       }
     }
-    if (shouldDelete) {
-      dispatch(entityDeleted({ entityIdentifier: { id, type: 'raster_layer' } }));
+    if (isControlAdapterLayer(l)) {
+      if (
+        l.controlAdapter.image?.name === imageDTO.image_name ||
+        l.controlAdapter.processedImage?.name === imageDTO.image_name
+      ) {
+        dispatch(layerDeleted(l.id));
+      }
+    }
+    if (isIPAdapterLayer(l)) {
+      if (l.ipAdapter.image?.name === imageDTO.image_name) {
+        dispatch(layerDeleted(l.id));
+      }
+    }
+    if (isInitialImageLayer(l)) {
+      if (l.image?.name === imageDTO.image_name) {
+        dispatch(layerDeleted(l.id));
+      }
     }
   });
 };
@@ -123,10 +145,14 @@ export const addImageDeletionListeners = (startAppListening: AppStartListening)
           }
         }
 
+        // We need to reset the features where the image is in use - none of these work if their image(s) don't exist
+        if (imageUsage.isCanvasImage) {
+          dispatch(resetCanvas());
+        }
+
+        deleteControlAdapterImages(state, dispatch, imageDTO);
         deleteNodesImages(state, dispatch, imageDTO);
-        // deleteControlAdapterImages(state, dispatch, imageDTO);
-        deleteIPAdapterImages(state, dispatch, imageDTO);
-        deleteLayerImages(state, dispatch, imageDTO);
+        deleteControlLayerImages(state, dispatch, imageDTO);
       } catch {
         // no-op
       } finally {
@@ -163,11 +189,14 @@ export const addImageDeletionListeners = (startAppListening: AppStartListening)
 
         // We need to reset the features where the image is in use - none of these work if their image(s) don't exist
 
+        if (imagesUsage.some((i) => i.isCanvasImage)) {
+          dispatch(resetCanvas());
+        }
+
         imageDTOs.forEach((imageDTO) => {
+          deleteControlAdapterImages(state, dispatch, imageDTO);
           deleteNodesImages(state, dispatch, imageDTO);
-          // deleteControlAdapterImages(state, dispatch, imageDTO);
-          deleteIPAdapterImages(state, dispatch, imageDTO);
-          deleteLayerImages(state, dispatch, imageDTO);
+          deleteControlLayerImages(state, dispatch, imageDTO);
         });
       } catch {
         // no-op
@@ -191,6 +220,7 @@ export const addImageDeletionListeners = (startAppListening: AppStartListening)
   startAppListening({
     matcher: imagesApi.endpoints.deleteImage.matchFulfilled,
     effect: (action) => {
+      const log = logger('images');
       log.debug({ imageDTO: action.meta.arg.originalArgs }, 'Image deleted');
     },
   });
@@ -198,6 +228,7 @@ export const addImageDeletionListeners = (startAppListening: AppStartListening)
   startAppListening({
     matcher: imagesApi.endpoints.deleteImage.matchRejected,
     effect: (action) => {
+      const log = logger('images');
       log.debug({ imageDTO: action.meta.arg.originalArgs }, 'Unable to delete image');
     },
   });

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

@@ -1,19 +1,28 @@
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { parseify } from 'common/util/serialize';
+import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
 import {
-  controlLayerAdded,
-  ipaImageChanged,
-  rasterLayerAdded,
-  rgIPAdapterImageChanged,
-} from 'features/controlLayers/store/canvasSlice';
-import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
-import type { CanvasControlLayerState, CanvasRasterLayerState } from 'features/controlLayers/store/types';
-import { imageDTOToImageObject } from 'features/controlLayers/store/types';
+  controlAdapterImageChanged,
+  controlAdapterIsEnabledChanged,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import {
+  caLayerImageChanged,
+  iiLayerImageChanged,
+  ipaLayerImageChanged,
+  rgLayerIPAdapterImageChanged,
+} from 'features/controlLayers/store/controlLayersSlice';
 import type { TypesafeDraggableData, TypesafeDroppableData } from 'features/dnd/types';
 import { isValidDrop } from 'features/dnd/util/isValidDrop';
-import { imageToCompareChanged, isImageViewerOpenChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
+import {
+  imageSelected,
+  imageToCompareChanged,
+  isImageViewerOpenChanged,
+  selectionChanged,
+} from 'features/gallery/store/gallerySlice';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
+import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { upscaleInitialImageChanged } from 'features/parameters/store/upscaleSlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -22,12 +31,11 @@ export const dndDropped = createAction<{
   activeData: TypesafeDraggableData;
 }>('dnd/dndDropped');
 
-const log = logger('system');
-
 export const addImageDroppedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: dndDropped,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
+      const log = logger('dnd');
       const { activeData, overData } = action.payload;
       if (!isValidDrop(overData, activeData)) {
         return;
@@ -38,22 +46,80 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
       } 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');
+        log.debug({ activeData: parseify(activeData), overData: parseify(overData) }, 'Node field dropped');
       } else {
         log.debug({ activeData, overData }, `Unknown payload dropped`);
       }
 
+      /**
+       * Image dropped on current image
+       */
+      if (
+        overData.actionType === 'SET_CURRENT_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        dispatch(imageSelected(activeData.payload.imageDTO));
+        dispatch(isImageViewerOpenChanged(true));
+        return;
+      }
+
+      /**
+       * Image dropped on ControlNet
+       */
+      if (
+        overData.actionType === 'SET_CONTROL_ADAPTER_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { id } = overData.context;
+        dispatch(
+          controlAdapterImageChanged({
+            id,
+            controlImage: activeData.payload.imageDTO.image_name,
+          })
+        );
+        dispatch(
+          controlAdapterIsEnabledChanged({
+            id,
+            isEnabled: true,
+          })
+        );
+        return;
+      }
+
+      /**
+       * Image dropped on Control Adapter Layer
+       */
+      if (
+        overData.actionType === 'SET_CA_LAYER_IMAGE' &&
+        activeData.payloadType === 'IMAGE_DTO' &&
+        activeData.payload.imageDTO
+      ) {
+        const { layerId } = overData.context;
+        dispatch(
+          caLayerImageChanged({
+            layerId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
+        return;
+      }
+
       /**
        * Image dropped on IP Adapter Layer
        */
       if (
-        overData.actionType === 'SET_IPA_IMAGE' &&
+        overData.actionType === 'SET_IPA_LAYER_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        const { id } = overData.context;
+        const { layerId } = overData.context;
         dispatch(
-          ipaImageChanged({ entityIdentifier: { id, type: 'ip_adapter' }, imageDTO: activeData.payload.imageDTO })
+          ipaLayerImageChanged({
+            layerId,
+            imageDTO: activeData.payload.imageDTO,
+          })
         );
         return;
       }
@@ -62,14 +128,14 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
        * Image dropped on RG Layer IP Adapter
        */
       if (
-        overData.actionType === 'SET_RG_IP_ADAPTER_IMAGE' &&
+        overData.actionType === 'SET_RG_LAYER_IP_ADAPTER_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        const { id, ipAdapterId } = overData.context;
+        const { layerId, ipAdapterId } = overData.context;
         dispatch(
-          rgIPAdapterImageChanged({
-            entityIdentifier: { id, type: 'regional_guidance' },
+          rgLayerIPAdapterImageChanged({
+            layerId,
             ipAdapterId,
             imageDTO: activeData.payload.imageDTO,
           })
@@ -78,38 +144,32 @@ export const addImageDroppedListener = (startAppListening: AppStartListening) =>
       }
 
       /**
-       * Image dropped on Raster layer
+       * Image dropped on II Layer Image
        */
       if (
-        overData.actionType === 'ADD_RASTER_LAYER_FROM_IMAGE' &&
+        overData.actionType === 'SET_II_LAYER_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 }));
+        const { layerId } = overData.context;
+        dispatch(
+          iiLayerImageChanged({
+            layerId,
+            imageDTO: activeData.payload.imageDTO,
+          })
+        );
         return;
       }
 
       /**
-       * Image dropped on Raster layer
+       * Image dropped on Canvas
        */
       if (
-        overData.actionType === 'ADD_CONTROL_LAYER_FROM_IMAGE' &&
+        overData.actionType === 'SET_CANVAS_INITIAL_IMAGE' &&
         activeData.payloadType === 'IMAGE_DTO' &&
         activeData.payload.imageDTO
       ) {
-        const imageObject = imageDTOToImageObject(activeData.payload.imageDTO);
-        const { x, y } = selectCanvasSlice(getState()).bbox.rect;
-        const overrides: Partial<CanvasControlLayerState> = {
-          objects: [imageObject],
-          position: { x, y },
-        };
-        dispatch(controlLayerAdded({ overrides, isSelected: true }));
+        dispatch(setInitialCanvasImage(activeData.payload.imageDTO, selectOptimalDimension(getState())));
         return;
       }
 

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

@@ -2,13 +2,13 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { imagesApi } from 'services/api/endpoints/images';
 
-const log = logger('gallery');
-
 export const addImageRemovedFromBoardFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.removeImageFromBoard.matchFulfilled,
     effect: (action) => {
+      const log = logger('images');
       const imageDTO = action.meta.arg.originalArgs;
+
       log.debug({ imageDTO }, 'Image removed from board');
     },
   });
@@ -16,7 +16,9 @@ export const addImageRemovedFromBoardFulfilledListener = (startAppListening: App
   startAppListening({
     matcher: imagesApi.endpoints.removeImageFromBoard.matchRejected,
     effect: (action) => {
+      const log = logger('images');
       const imageDTO = action.meta.arg.originalArgs;
+
       log.debug({ imageDTO }, 'Problem removing image from board');
     },
   });

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

@@ -6,17 +6,16 @@ import { imagesToDeleteSelected, isModalOpenChanged } from 'features/deleteImage
 export const addImageToDeleteSelectedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: imagesToDeleteSelected,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const imageDTOs = action.payload;
       const state = getState();
       const { shouldConfirmOnDelete } = state.system;
       const imagesUsage = selectImageUsage(getState());
 
       const isImageInUse =
-        imagesUsage.some((i) => i.isLayerImage) ||
-        imagesUsage.some((i) => i.isControlAdapterImage) ||
-        imagesUsage.some((i) => i.isIPAdapterImage) ||
-        imagesUsage.some((i) => i.isLayerImage);
+        imagesUsage.some((i) => i.isCanvasImage) ||
+        imagesUsage.some((i) => i.isControlImage) ||
+        imagesUsage.some((i) => i.isNodesImage);
 
       if (shouldConfirmOnDelete || isImageInUse) {
         dispatch(isModalOpenChanged(true));

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

@@ -1,8 +1,19 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { ipaImageChanged, rgIPAdapterImageChanged } from 'features/controlLayers/store/canvasSlice';
+import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
+import {
+  controlAdapterImageChanged,
+  controlAdapterIsEnabledChanged,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import {
+  caLayerImageChanged,
+  iiLayerImageChanged,
+  ipaLayerImageChanged,
+  rgLayerIPAdapterImageChanged,
+} from 'features/controlLayers/store/controlLayersSlice';
 import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { fieldImageValueChanged } from 'features/nodes/store/nodesSlice';
+import { selectOptimalDimension } from 'features/parameters/store/generationSlice';
 import { upscaleInitialImageChanged } from 'features/parameters/store/upscaleSlice';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
@@ -10,12 +21,11 @@ import { omit } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
 
-const log = logger('gallery');
-
 export const addImageUploadedFulfilledListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.uploadImage.matchFulfilled,
     effect: (action, { dispatch, getState }) => {
+      const log = logger('images');
       const imageDTO = action.payload;
       const state = getState();
       const { autoAddBoardId } = state.gallery;
@@ -71,6 +81,15 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         return;
       }
 
+      if (postUploadAction?.type === 'SET_CANVAS_INITIAL_IMAGE') {
+        dispatch(setInitialCanvasImage(imageDTO, selectOptimalDimension(state)));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setAsCanvasInitialImage'),
+        });
+        return;
+      }
+
       if (postUploadAction?.type === 'SET_UPSCALE_INITIAL_IMAGE') {
         dispatch(upscaleInitialImageChanged(imageDTO));
         toast({
@@ -80,33 +99,70 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
         return;
       }
 
-      // if (postUploadAction?.type === 'SET_CA_IMAGE') {
-      //   const { id } = postUploadAction;
-      //   dispatch(caImageChanged({ id, imageDTO }));
-      //   toast({ ...DEFAULT_UPLOADED_TOAST, description: t('toast.setControlImage') });
-      //   return;
-      // }
-
-      if (postUploadAction?.type === 'SET_IPA_IMAGE') {
+      if (postUploadAction?.type === 'SET_CONTROL_ADAPTER_IMAGE') {
         const { id } = postUploadAction;
-        dispatch(ipaImageChanged({ entityIdentifier: { id, type: 'ip_adapter' }, imageDTO }));
-        toast({ ...DEFAULT_UPLOADED_TOAST, description: t('toast.setControlImage') });
+        dispatch(
+          controlAdapterIsEnabledChanged({
+            id,
+            isEnabled: true,
+          })
+        );
+        dispatch(
+          controlAdapterImageChanged({
+            id,
+            controlImage: imageDTO.image_name,
+          })
+        );
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
         return;
       }
 
-      if (postUploadAction?.type === 'SET_RG_IP_ADAPTER_IMAGE') {
-        const { id, ipAdapterId } = postUploadAction;
-        dispatch(
-          rgIPAdapterImageChanged({ entityIdentifier: { id, type: 'regional_guidance' }, ipAdapterId, imageDTO })
-        );
-        toast({ ...DEFAULT_UPLOADED_TOAST, description: t('toast.setControlImage') });
-        return;
+      if (postUploadAction?.type === 'SET_CA_LAYER_IMAGE') {
+        const { layerId } = postUploadAction;
+        dispatch(caLayerImageChanged({ layerId, imageDTO }));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
+      }
+
+      if (postUploadAction?.type === 'SET_IPA_LAYER_IMAGE') {
+        const { layerId } = postUploadAction;
+        dispatch(ipaLayerImageChanged({ layerId, imageDTO }));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
+      }
+
+      if (postUploadAction?.type === 'SET_RG_LAYER_IP_ADAPTER_IMAGE') {
+        const { layerId, ipAdapterId } = postUploadAction;
+        dispatch(rgLayerIPAdapterImageChanged({ layerId, ipAdapterId, imageDTO }));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
+      }
+
+      if (postUploadAction?.type === 'SET_II_LAYER_IMAGE') {
+        const { layerId } = postUploadAction;
+        dispatch(iiLayerImageChanged({ layerId, imageDTO }));
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: t('toast.setControlImage'),
+        });
       }
 
       if (postUploadAction?.type === 'SET_NODES_IMAGE') {
         const { nodeId, fieldName } = postUploadAction;
         dispatch(fieldImageValueChanged({ nodeId, fieldName, value: imageDTO }));
-        toast({ ...DEFAULT_UPLOADED_TOAST, description: `${t('toast.setNodeField')} ${fieldName}` });
+        toast({
+          ...DEFAULT_UPLOADED_TOAST,
+          description: `${t('toast.setNodeField')} ${fieldName}`,
+        });
         return;
       }
     },
@@ -115,6 +171,7 @@ export const addImageUploadedFulfilledListener = (startAppListening: AppStartLis
   startAppListening({
     matcher: imagesApi.endpoints.uploadImage.matchRejected,
     effect: (action) => {
+      const log = logger('images');
       const sanitizedData = {
         arg: {
           ...omit(action.meta.arg.originalArgs, ['file', 'postUploadAction']),

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

@@ -6,7 +6,7 @@ import type { ImageDTO } from 'services/api/types';
 export const addImagesStarredListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.starImages.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const { updated_image_names: starredImages } = action.payload;
 
       const state = getState();

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

@@ -6,7 +6,7 @@ import type { ImageDTO } from 'services/api/types';
 export const addImagesUnstarredListener = (startAppListening: AppStartListening) => {
   startAppListening({
     matcher: imagesApi.endpoints.unstarImages.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
+    effect: async (action, { dispatch, getState }) => {
       const { updated_image_names: unstarredImages } = action.payload;
 
       const state = getState();

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

@@ -1,18 +1,23 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { loraDeleted } from 'features/controlLayers/store/lorasSlice';
-import { modelChanged, vaeSelected } from 'features/controlLayers/store/paramsSlice';
+import {
+  controlAdapterIsEnabledChanged,
+  selectControlAdapterAll,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { loraRemoved } from 'features/lora/store/loraSlice';
 import { modelSelected } from 'features/parameters/store/actions';
+import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
 import { zParameterModel } from 'features/parameters/types/parameterSchemas';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
-
-const log = logger('models');
+import { forEach } from 'lodash-es';
 
 export const addModelSelectedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: modelSelected,
     effect: (action, { getState, dispatch }) => {
+      const log = logger('models');
+
       const state = getState();
       const result = zParameterModel.safeParse(action.payload);
 
@@ -24,36 +29,34 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
       const newModel = result.data;
 
       const newBaseModel = newModel.base;
-      const didBaseModelChange = state.params.model?.base !== newBaseModel;
+      const didBaseModelChange = state.generation.model?.base !== newBaseModel;
 
       if (didBaseModelChange) {
         // we may need to reset some incompatible submodels
         let modelsCleared = 0;
 
         // handle incompatible loras
-        state.loras.loras.forEach((lora) => {
+        forEach(state.lora.loras, (lora, id) => {
           if (lora.model.base !== newBaseModel) {
-            dispatch(loraDeleted({ id: lora.id }));
+            dispatch(loraRemoved(id));
             modelsCleared += 1;
           }
         });
 
         // handle incompatible vae
-        const { vae } = state.params;
+        const { vae } = state.generation;
         if (vae && vae.base !== newBaseModel) {
           dispatch(vaeSelected(null));
           modelsCleared += 1;
         }
 
         // handle incompatible controlnets
-        // state.canvas.present.controlAdapters.entities.forEach((ca) => {
-        //   if (ca.model?.base !== newBaseModel) {
-        //     modelsCleared += 1;
-        //     if (ca.isEnabled) {
-        //       dispatch(entityIsEnabledToggled({ entityIdentifier: { id: ca.id, type: 'control_adapter' } }));
-        //     }
-        //   }
-        // });
+        selectControlAdapterAll(state.controlAdapters).forEach((ca) => {
+          if (ca.model?.base !== newBaseModel) {
+            dispatch(controlAdapterIsEnabledChanged({ id: ca.id, isEnabled: false }));
+            modelsCleared += 1;
+          }
+        });
 
         if (modelsCleared > 0) {
           toast({
@@ -67,7 +70,7 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
         }
       }
 
-      dispatch(modelChanged({ model: newModel, previousModel: state.params.model }));
+      dispatch(modelChanged(newModel, state.generation.model));
     },
   });
 };

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

@@ -1,42 +1,36 @@
 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 type { JSONObject } from 'common/types';
 import {
-  bboxHeightChanged,
-  bboxWidthChanged,
-  controlLayerModelChanged,
-  ipaModelChanged,
-  rgIPAdapterModelChanged,
-} from 'features/controlLayers/store/canvasSlice';
-import { loraDeleted } from 'features/controlLayers/store/lorasSlice';
-import { modelChanged, refinerModelChanged, vaeSelected } from 'features/controlLayers/store/paramsSlice';
-import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
-import { getEntityIdentifier } from 'features/controlLayers/store/types';
-import { calculateNewSize } from 'features/parameters/components/DocumentSize/calculateNewSize';
+  controlAdapterModelCleared,
+  selectControlAdapterAll,
+} from 'features/controlAdapters/store/controlAdaptersSlice';
+import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
+import { loraRemoved } from 'features/lora/store/loraSlice';
+import { calculateNewSize } from 'features/parameters/components/ImageSize/calculateNewSize';
+import { modelChanged, vaeSelected } from 'features/parameters/store/generationSlice';
 import { postProcessingModelChanged, upscaleModelChanged } from 'features/parameters/store/upscaleSlice';
 import { zParameterModel, zParameterVAEModel } from 'features/parameters/types/parameterSchemas';
 import { getIsSizeOptimal, getOptimalDimension } from 'features/parameters/util/optimalDimension';
+import { refinerModelChanged } from 'features/sdxl/store/sdxlSlice';
+import { forEach } from 'lodash-es';
 import type { Logger } from 'roarr';
 import { modelConfigsAdapterSelectors, modelsApi } from 'services/api/endpoints/models';
 import type { AnyModelConfig } from 'services/api/types';
 import {
-  isControlNetOrT2IAdapterModelConfig,
-  isIPAdapterModelConfig,
-  isLoRAModelConfig,
   isNonRefinerMainModelConfig,
   isRefinerMainModelModelConfig,
   isSpandrelImageToImageModelConfig,
   isVAEModelConfig,
 } from 'services/api/types';
 
-const log = logger('models');
-
 export const addModelsLoadedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     predicate: modelsApi.endpoints.getModelConfigs.matchFulfilled,
-    effect: (action, { getState, dispatch }) => {
+    effect: async (action, { getState, dispatch }) => {
       // models loaded, we need to ensure the selected model is available and if not, select the first one
+      const log = logger('models');
       log.info({ models: action.payload.entities }, `Models loaded (${action.payload.ids.length})`);
 
       const state = getState();
@@ -49,7 +43,6 @@ export const addModelsLoadedListener = (startAppListening: AppStartListening) =>
       handleLoRAModels(models, state, dispatch, log);
       handleControlAdapterModels(models, state, dispatch, log);
       handleSpandrelImageToImageModels(models, state, dispatch, log);
-      handleIPAdapterModels(models, state, dispatch, log);
     },
   });
 };
@@ -58,15 +51,15 @@ type ModelHandler = (
   models: AnyModelConfig[],
   state: RootState,
   dispatch: AppDispatch,
-  log: Logger<SerializableObject>
+  log: Logger<JSONObject>
 ) => undefined;
 
 const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
-  const currentModel = state.params.model;
+  const currentModel = state.generation.model;
   const mainModels = models.filter(isNonRefinerMainModelConfig);
   if (mainModels.length === 0) {
     // No models loaded at all
-    dispatch(modelChanged({ model: null }));
+    dispatch(modelChanged(null));
     return;
   }
 
@@ -81,16 +74,25 @@ const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
   if (defaultModelInList) {
     const result = zParameterModel.safeParse(defaultModelInList);
     if (result.success) {
-      dispatch(modelChanged({ model: defaultModelInList, previousModel: currentModel }));
-      const { bbox } = selectCanvasSlice(state);
+      dispatch(modelChanged(defaultModelInList, currentModel));
+
       const optimalDimension = getOptimalDimension(defaultModelInList);
-      if (getIsSizeOptimal(bbox.rect.width, bbox.rect.height, optimalDimension)) {
+      if (
+        getIsSizeOptimal(
+          state.controlLayers.present.size.width,
+          state.controlLayers.present.size.height,
+          optimalDimension
+        )
+      ) {
         return;
       }
-      const { width, height } = calculateNewSize(bbox.aspectRatio.value, optimalDimension * optimalDimension);
+      const { width, height } = calculateNewSize(
+        state.controlLayers.present.size.aspectRatio.value,
+        optimalDimension * optimalDimension
+      );
 
-      dispatch(bboxWidthChanged({ width }));
-      dispatch(bboxHeightChanged({ height }));
+      dispatch(widthChanged({ width }));
+      dispatch(heightChanged({ height }));
       return;
     }
   }
@@ -102,11 +104,11 @@ const handleMainModels: ModelHandler = (models, state, dispatch, log) => {
     return;
   }
 
-  dispatch(modelChanged({ model: result.data, previousModel: currentModel }));
+  dispatch(modelChanged(result.data, currentModel));
 };
 
 const handleRefinerModels: ModelHandler = (models, state, dispatch, _log) => {
-  const currentRefinerModel = state.params.refinerModel;
+  const currentRefinerModel = state.sdxl.refinerModel;
   const refinerModels = models.filter(isRefinerMainModelModelConfig);
   if (models.length === 0) {
     // No models loaded at all
@@ -125,7 +127,7 @@ const handleRefinerModels: ModelHandler = (models, state, dispatch, _log) => {
 };
 
 const handleVAEModels: ModelHandler = (models, state, dispatch, log) => {
-  const currentVae = state.params.vae;
+  const currentVae = state.generation.vae;
 
   if (currentVae === null) {
     // null is a valid VAE! it means "use the default with the main model"
@@ -158,47 +160,28 @@ const handleVAEModels: ModelHandler = (models, state, dispatch, log) => {
 };
 
 const handleLoRAModels: ModelHandler = (models, state, dispatch, _log) => {
-  const loraModels = models.filter(isLoRAModelConfig);
-  state.loras.loras.forEach((lora) => {
-    const isLoRAAvailable = loraModels.some((m) => m.key === lora.model.key);
+  const loras = state.lora.loras;
+
+  forEach(loras, (lora, id) => {
+    const isLoRAAvailable = models.some((m) => m.key === lora.model.key);
+
     if (isLoRAAvailable) {
       return;
     }
-    dispatch(loraDeleted({ id: lora.id }));
+
+    dispatch(loraRemoved(id));
   });
 };
 
 const handleControlAdapterModels: ModelHandler = (models, state, dispatch, _log) => {
-  const caModels = models.filter(isControlNetOrT2IAdapterModelConfig);
-  selectCanvasSlice(state).controlLayers.entities.forEach((entity) => {
-    const isModelAvailable = caModels.some((m) => m.key === entity.controlAdapter.model?.key);
+  selectControlAdapterAll(state.controlAdapters).forEach((ca) => {
+    const isModelAvailable = models.some((m) => m.key === ca.model?.key);
+
     if (isModelAvailable) {
       return;
     }
-    dispatch(controlLayerModelChanged({ entityIdentifier: getEntityIdentifier(entity), modelConfig: null }));
-  });
-};
 
-const handleIPAdapterModels: ModelHandler = (models, state, dispatch, _log) => {
-  const ipaModels = models.filter(isIPAdapterModelConfig);
-  selectCanvasSlice(state).ipAdapters.entities.forEach((entity) => {
-    const isModelAvailable = ipaModels.some((m) => m.key === entity.ipAdapter.model?.key);
-    if (isModelAvailable) {
-      return;
-    }
-    dispatch(ipaModelChanged({ entityIdentifier: getEntityIdentifier(entity), modelConfig: null }));
-  });
-
-  selectCanvasSlice(state).regions.entities.forEach((entity) => {
-    entity.ipAdapters.forEach(({ id: ipAdapterId, model }) => {
-      const isModelAvailable = ipaModels.some((m) => m.key === model?.key);
-      if (isModelAvailable) {
-        return;
-      }
-      dispatch(
-        rgIPAdapterModelChanged({ entityIdentifier: getEntityIdentifier(entity), ipAdapterId, modelConfig: null })
-      );
-    });
+    dispatch(controlAdapterModelCleared({ id: ca.id }));
   });
 };
 

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

@@ -1,6 +1,6 @@
 import { isAnyOf } from '@reduxjs/toolkit';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { positivePromptChanged } from 'features/controlLayers/store/paramsSlice';
+import { positivePromptChanged } from 'features/controlLayers/store/controlLayersSlice';
 import {
   combinatorialToggled,
   isErrorChanged,
@@ -15,7 +15,7 @@ import { getPresetModifiedPrompts } from 'features/nodes/util/graph/graphBuilder
 import { activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { stylePresetsApi } from 'services/api/endpoints/stylePresets';
 import { utilitiesApi } from 'services/api/endpoints/utilities';
-import { socketConnected } from 'services/events/setEventListeners';
+import { socketConnected } from 'services/events/actions';
 
 const matcher = isAnyOf(
   positivePromptChanged,
@@ -24,6 +24,8 @@ const matcher = isAnyOf(
   maxPromptsReset,
   socketConnected,
   activeStylePresetIdChanged,
+  stylePresetsApi.endpoints.deleteStylePreset.matchFulfilled,
+  stylePresetsApi.endpoints.updateStylePreset.matchFulfilled,
   stylePresetsApi.endpoints.listStylePresets.matchFulfilled
 );
 

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

@@ -1,5 +1,6 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { bboxHeightChanged, bboxWidthChanged } from 'features/controlLayers/store/canvasSlice';
+import { heightChanged, widthChanged } from 'features/controlLayers/store/controlLayersSlice';
+import { setDefaultSettings } from 'features/parameters/store/actions';
 import {
   setCfgRescaleMultiplier,
   setCfgScale,
@@ -7,8 +8,7 @@ import {
   setSteps,
   vaePrecisionChanged,
   vaeSelected,
-} from 'features/controlLayers/store/paramsSlice';
-import { setDefaultSettings } from 'features/parameters/store/actions';
+} from 'features/parameters/store/generationSlice';
 import {
   isParameterCFGRescaleMultiplier,
   isParameterCFGScale,
@@ -30,7 +30,7 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
     effect: async (action, { dispatch, getState }) => {
       const state = getState();
 
-      const currentModel = state.params.model;
+      const currentModel = state.generation.model;
 
       if (!currentModel) {
         return;
@@ -98,13 +98,13 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
         const setSizeOptions = { updateAspectRatio: true, clamp: true };
         if (width) {
           if (isParameterWidth(width)) {
-            dispatch(bboxWidthChanged({ width, ...setSizeOptions }));
+            dispatch(widthChanged({ width, ...setSizeOptions }));
           }
         }
 
         if (height) {
           if (isParameterHeight(height)) {
-            dispatch(bboxHeightChanged({ height, ...setSizeOptions }));
+            dispatch(heightChanged({ height, ...setSizeOptions }));
           }
         }
 

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

@@ -6,9 +6,9 @@ import { atom } from 'nanostores';
 import { api } from 'services/api';
 import { modelsApi } from 'services/api/endpoints/models';
 import { queueApi, selectQueueStatus } from 'services/api/endpoints/queue';
-import { socketConnected } from 'services/events/setEventListeners';
+import { socketConnected } from 'services/events/actions';
 
-const log = logger('events');
+const log = logger('socketio');
 
 const $isFirstConnection = atom(true);
 

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

@@ -0,0 +1,14 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { socketDisconnected } from 'services/events/actions';
+
+const log = logger('socketio');
+
+export const addSocketDisconnectedEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketDisconnected,
+    effect: () => {
+      log.debug('Disconnected');
+    },
+  });
+};

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

@@ -0,0 +1,26 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
+import { parseify } from 'common/util/serialize';
+import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
+import { zNodeStatus } from 'features/nodes/types/invocation';
+import { socketGeneratorProgress } from 'services/events/actions';
+
+const log = logger('socketio');
+
+export const addGeneratorProgressEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketGeneratorProgress,
+    effect: (action) => {
+      log.trace(parseify(action.payload), `Generator progress`);
+      const { invocation_source_id, step, total_steps, progress_image } = action.payload.data;
+      const nes = deepClone($nodeExecutionStates.get()[invocation_source_id]);
+      if (nes) {
+        nes.status = zNodeStatus.enum.IN_PROGRESS;
+        nes.progress = (step + 1) / total_steps;
+        nes.progressImage = progress_image ?? null;
+        upsertExecutionState(nes.nodeId, nes);
+      }
+    },
+  });
+};

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

@@ -0,0 +1,122 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
+import { parseify } from 'common/util/serialize';
+import { addImageToStagingArea } from 'features/canvas/store/canvasSlice';
+import {
+  boardIdSelected,
+  galleryViewChanged,
+  imageSelected,
+  isImageViewerOpenChanged,
+  offsetChanged,
+} from 'features/gallery/store/gallerySlice';
+import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
+import { zNodeStatus } from 'features/nodes/types/invocation';
+import { CANVAS_OUTPUT } from 'features/nodes/util/graph/constants';
+import { boardsApi } from 'services/api/endpoints/boards';
+import { imagesApi } from 'services/api/endpoints/images';
+import { getCategories, getListImagesUrl } from 'services/api/util';
+import { socketInvocationComplete } from 'services/events/actions';
+
+// These nodes output an image, but do not actually *save* an image, so we don't want to handle the gallery logic on them
+const nodeTypeDenylist = ['load_image', 'image'];
+
+const log = logger('socketio');
+
+export const addInvocationCompleteEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketInvocationComplete,
+    effect: async (action, { dispatch, getState }) => {
+      const { data } = action.payload;
+      log.debug({ data: parseify(data) }, `Invocation complete (${data.invocation.type})`);
+
+      const { result, invocation_source_id } = data;
+      // This complete event has an associated image output
+      if (data.result.type === 'image_output' && !nodeTypeDenylist.includes(data.invocation.type)) {
+        const { image_name } = data.result.image;
+        const { canvas, gallery } = getState();
+
+        // This populates the `getImageDTO` cache
+        const imageDTORequest = dispatch(
+          imagesApi.endpoints.getImageDTO.initiate(image_name, {
+            forceRefetch: true,
+          })
+        );
+
+        const imageDTO = await imageDTORequest.unwrap();
+        imageDTORequest.unsubscribe();
+
+        // Add canvas images to the staging area
+        if (canvas.batchIds.includes(data.batch_id) && data.invocation_source_id === CANVAS_OUTPUT) {
+          dispatch(addImageToStagingArea(imageDTO));
+        }
+
+        if (!imageDTO.is_intermediate) {
+          // update the total images for the board
+          dispatch(
+            boardsApi.util.updateQueryData('getBoardImagesTotal', imageDTO.board_id ?? 'none', (draft) => {
+              // eslint-disable-next-line @typescript-eslint/no-unused-vars
+              draft.total += 1;
+            })
+          );
+
+          dispatch(
+            imagesApi.util.invalidateTags([
+              { type: 'Board', id: imageDTO.board_id ?? 'none' },
+              {
+                type: 'ImageList',
+                id: getListImagesUrl({
+                  board_id: imageDTO.board_id ?? 'none',
+                  categories: getCategories(imageDTO),
+                }),
+              },
+            ])
+          );
+
+          const { shouldAutoSwitch } = gallery;
+
+          // If auto-switch is enabled, select the new image
+          if (shouldAutoSwitch) {
+            // if auto-add is enabled, switch the gallery view and board if needed as the image comes in
+            if (gallery.galleryView !== 'images') {
+              dispatch(galleryViewChanged('images'));
+            }
+
+            if (imageDTO.board_id && imageDTO.board_id !== gallery.selectedBoardId) {
+              dispatch(
+                boardIdSelected({
+                  boardId: imageDTO.board_id,
+                  selectedImageName: imageDTO.image_name,
+                })
+              );
+            }
+
+            dispatch(offsetChanged({ offset: 0 }));
+
+            if (!imageDTO.board_id && gallery.selectedBoardId !== 'none') {
+              dispatch(
+                boardIdSelected({
+                  boardId: 'none',
+                  selectedImageName: imageDTO.image_name,
+                })
+              );
+            }
+
+            dispatch(imageSelected(imageDTO));
+            dispatch(isImageViewerOpenChanged(true));
+          }
+        }
+      }
+
+      const nes = deepClone($nodeExecutionStates.get()[invocation_source_id]);
+      if (nes) {
+        nes.status = zNodeStatus.enum.COMPLETED;
+        if (nes.progress !== null) {
+          nes.progress = 1;
+        }
+        nes.outputs.push(result);
+        upsertExecutionState(nes.nodeId, nes);
+      }
+    },
+  });
+};

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

@@ -0,0 +1,31 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
+import { parseify } from 'common/util/serialize';
+import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
+import { zNodeStatus } from 'features/nodes/types/invocation';
+import { socketInvocationError } from 'services/events/actions';
+
+const log = logger('socketio');
+
+export const addInvocationErrorEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketInvocationError,
+    effect: (action) => {
+      const { invocation_source_id, invocation, error_type, error_message, error_traceback } = action.payload.data;
+      log.error(parseify(action.payload), `Invocation error (${invocation.type})`);
+      const nes = deepClone($nodeExecutionStates.get()[invocation_source_id]);
+      if (nes) {
+        nes.status = zNodeStatus.enum.FAILED;
+        nes.progress = null;
+        nes.progressImage = null;
+        nes.error = {
+          error_type,
+          error_message,
+          error_traceback,
+        };
+        upsertExecutionState(nes.nodeId, nes);
+      }
+    },
+  });
+};

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

@@ -0,0 +1,24 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
+import { parseify } from 'common/util/serialize';
+import { $nodeExecutionStates, upsertExecutionState } from 'features/nodes/hooks/useExecutionState';
+import { zNodeStatus } from 'features/nodes/types/invocation';
+import { socketInvocationStarted } from 'services/events/actions';
+
+const log = logger('socketio');
+
+export const addInvocationStartedEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketInvocationStarted,
+    effect: (action) => {
+      log.debug(parseify(action.payload), `Invocation started (${action.payload.data.invocation.type})`);
+      const { invocation_source_id } = action.payload.data;
+      const nes = deepClone($nodeExecutionStates.get()[invocation_source_id]);
+      if (nes) {
+        nes.status = zNodeStatus.enum.IN_PROGRESS;
+        upsertExecutionState(nes.nodeId, nes);
+      }
+    },
+  });
+};

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

@@ -0,0 +1,196 @@
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { api, LIST_TAG } from 'services/api';
+import { modelsApi } from 'services/api/endpoints/models';
+import {
+  socketModelInstallCancelled,
+  socketModelInstallComplete,
+  socketModelInstallDownloadProgress,
+  socketModelInstallDownloadsComplete,
+  socketModelInstallDownloadStarted,
+  socketModelInstallError,
+  socketModelInstallStarted,
+} from 'services/events/actions';
+
+/**
+ * A model install has two main stages - downloading and installing. All these events are namespaced under `model_install_`
+ * which is a bit misleading. For example, a `model_install_started` event is actually fired _after_ the model has fully
+ * downloaded and is being "physically" installed.
+ *
+ * Note: the download events are only fired for remote model installs, not local.
+ *
+ * Here's the expected flow:
+ * - API receives install request, model manager preps the install
+ * - `model_install_download_started` fired when the download starts
+ * - `model_install_download_progress` fired continually until the download is complete
+ * - `model_install_download_complete` fired when the download is complete
+ * - `model_install_started` fired when the "physical" installation starts
+ * - `model_install_complete` fired when the installation is complete
+ * - `model_install_cancelled` fired if the installation is cancelled
+ * - `model_install_error` fired if the installation has an error
+ */
+
+const selectModelInstalls = modelsApi.endpoints.listModelInstalls.select();
+
+export const addModelInstallEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketModelInstallDownloadStarted,
+    effect: async (action, { dispatch, getState }) => {
+      const { id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.status = 'downloading';
+            }
+            return draft;
+          })
+        );
+      }
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelInstallStarted,
+    effect: async (action, { dispatch, getState }) => {
+      const { id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.status = 'running';
+            }
+            return draft;
+          })
+        );
+      }
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelInstallDownloadProgress,
+    effect: async (action, { dispatch, getState }) => {
+      const { bytes, total_bytes, id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.bytes = bytes;
+              modelImport.total_bytes = total_bytes;
+              modelImport.status = 'downloading';
+            }
+            return draft;
+          })
+        );
+      }
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelInstallComplete,
+    effect: (action, { dispatch, getState }) => {
+      const { id } = action.payload.data;
+
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.status = 'completed';
+            }
+            return draft;
+          })
+        );
+      }
+
+      dispatch(api.util.invalidateTags([{ type: 'ModelConfig', id: LIST_TAG }]));
+      dispatch(api.util.invalidateTags([{ type: 'ModelScanFolderResults', id: LIST_TAG }]));
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelInstallError,
+    effect: (action, { dispatch, getState }) => {
+      const { id, error, error_type } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.status = 'error';
+              modelImport.error_reason = error_type;
+              modelImport.error = error;
+            }
+            return draft;
+          })
+        );
+      }
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelInstallCancelled,
+    effect: (action, { dispatch, getState }) => {
+      const { id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.status = 'cancelled';
+            }
+            return draft;
+          })
+        );
+      }
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelInstallDownloadsComplete,
+    effect: (action, { dispatch, getState }) => {
+      const { id } = action.payload.data;
+      const { data } = selectModelInstalls(getState());
+
+      if (!data || !data.find((m) => m.id === id)) {
+        dispatch(api.util.invalidateTags([{ type: 'ModelInstalls' }]));
+      } else {
+        dispatch(
+          modelsApi.util.updateQueryData('listModelInstalls', undefined, (draft) => {
+            const modelImport = draft.find((m) => m.id === id);
+            if (modelImport) {
+              modelImport.status = 'downloads_done';
+            }
+            return draft;
+          })
+        );
+      }
+    },
+  });
+};

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

@@ -0,0 +1,42 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { socketModelLoadComplete, socketModelLoadStarted } from 'services/events/actions';
+
+const log = logger('socketio');
+
+export const addModelLoadEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketModelLoadStarted,
+    effect: (action) => {
+      const { config, submodel_type } = action.payload.data;
+      const { name, base, type } = config;
+
+      const extras: string[] = [base, type];
+
+      if (submodel_type) {
+        extras.push(submodel_type);
+      }
+
+      const message = `Model load started: ${name} (${extras.join(', ')})`;
+
+      log.debug(action.payload, message);
+    },
+  });
+
+  startAppListening({
+    actionCreator: socketModelLoadComplete,
+    effect: (action) => {
+      const { config, submodel_type } = action.payload.data;
+      const { name, base, type } = config;
+
+      const extras: string[] = [base, type];
+      if (submodel_type) {
+        extras.push(submodel_type);
+      }
+
+      const message = `Model load complete: ${name} (${extras.join(', ')})`;
+
+      log.debug(action.payload, message);
+    },
+  });
+};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketio/socketQueueItemStatusChanged.tsx

@@ -0,0 +1,114 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { deepClone } from 'common/util/deepClone';
+import { $nodeExecutionStates } from 'features/nodes/hooks/useExecutionState';
+import { zNodeStatus } from 'features/nodes/types/invocation';
+import ErrorToastDescription, { getTitleFromErrorType } from 'features/toast/ErrorToastDescription';
+import { toast } from 'features/toast/toast';
+import { forEach } from 'lodash-es';
+import { queueApi, queueItemsAdapter } from 'services/api/endpoints/queue';
+import { socketQueueItemStatusChanged } from 'services/events/actions';
+
+const log = logger('socketio');
+
+export const addSocketQueueItemStatusChangedEventListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: socketQueueItemStatusChanged,
+    effect: async (action, { dispatch, getState }) => {
+      // we've got new status for the queue item, batch and queue
+      const {
+        item_id,
+        session_id,
+        status,
+        started_at,
+        updated_at,
+        completed_at,
+        batch_status,
+        queue_status,
+        error_type,
+        error_message,
+        error_traceback,
+      } = action.payload.data;
+
+      log.debug(action.payload, `Queue item ${item_id} status updated: ${status}`);
+
+      // Update this specific queue item in the list of queue items (this is the queue item DTO, without the session)
+      dispatch(
+        queueApi.util.updateQueryData('listQueueItems', undefined, (draft) => {
+          queueItemsAdapter.updateOne(draft, {
+            id: String(item_id),
+            changes: {
+              status,
+              started_at,
+              updated_at: updated_at ?? undefined,
+              completed_at: completed_at ?? undefined,
+              error_type,
+              error_message,
+              error_traceback,
+            },
+          });
+        })
+      );
+
+      // Update the queue status (we do not get the processor status here)
+      dispatch(
+        queueApi.util.updateQueryData('getQueueStatus', undefined, (draft) => {
+          if (!draft) {
+            return;
+          }
+          Object.assign(draft.queue, queue_status);
+        })
+      );
+
+      // Update the batch status
+      dispatch(
+        queueApi.util.updateQueryData('getBatchStatus', { batch_id: batch_status.batch_id }, () => batch_status)
+      );
+
+      // Invalidate caches for things we cannot update
+      // TODO: technically, we could possibly update the current session queue item, but feels safer to just request it again
+      dispatch(
+        queueApi.util.invalidateTags([
+          'CurrentSessionQueueItem',
+          'NextSessionQueueItem',
+          'InvocationCacheStatus',
+          { type: 'SessionQueueItem', id: item_id },
+        ])
+      );
+
+      if (status === 'in_progress') {
+        forEach($nodeExecutionStates.get(), (nes) => {
+          if (!nes) {
+            return;
+          }
+          const clone = deepClone(nes);
+          clone.status = zNodeStatus.enum.PENDING;
+          clone.error = null;
+          clone.progress = null;
+          clone.progressImage = null;
+          clone.outputs = [];
+          $nodeExecutionStates.setKey(clone.nodeId, clone);
+        });
+      } else if (status === 'failed' && error_type) {
+        const isLocal = getState().config.isLocal ?? true;
+        const sessionId = session_id;
+
+        toast({
+          id: `INVOCATION_ERROR_${error_type}`,
+          title: getTitleFromErrorType(error_type),
+          status: 'error',
+          duration: null,
+          updateDescription: isLocal,
+          description: (
+            <ErrorToastDescription
+              errorType={error_type}
+              errorMessage={error_message}
+              sessionId={sessionId}
+              isLocal={isLocal}
+            />
+          ),
+        });
+      }
+    },
+  });
+};

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

@@ -0,0 +1,43 @@
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { stagingAreaImageSaved } from 'features/canvas/store/actions';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+import { imagesApi } from 'services/api/endpoints/images';
+
+export const addStagingAreaImageSavedListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: stagingAreaImageSaved,
+    effect: async (action, { dispatch, getState }) => {
+      const { imageDTO } = action.payload;
+
+      try {
+        const newImageDTO = await dispatch(
+          imagesApi.endpoints.changeImageIsIntermediate.initiate({
+            imageDTO,
+            is_intermediate: false,
+          })
+        ).unwrap();
+
+        // we may need to add it to the autoadd board
+        const { autoAddBoardId } = getState().gallery;
+
+        if (autoAddBoardId && autoAddBoardId !== 'none') {
+          await dispatch(
+            imagesApi.endpoints.addImageToBoard.initiate({
+              imageDTO: newImageDTO,
+              board_id: autoAddBoardId,
+            })
+          );
+        }
+        toast({ id: 'IMAGE_SAVED', title: t('toast.imageSaved'), status: 'success' });
+      } catch (error) {
+        toast({
+          id: 'IMAGE_SAVE_FAILED',
+          title: t('toast.imageSavingFailed'),
+          description: (error as Error)?.message,
+          status: 'error',
+        });
+      }
+    },
+  });
+};

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

@@ -2,20 +2,18 @@ import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { updateAllNodesRequested } from 'features/nodes/store/actions';
 import { $templates, nodesChanged } from 'features/nodes/store/nodesSlice';
-import { selectNodes } from 'features/nodes/store/selectors';
 import { NodeUpdateError } from 'features/nodes/types/error';
 import { isInvocationNode } from 'features/nodes/types/invocation';
 import { getNeedsUpdate, updateNode } from 'features/nodes/util/node/nodeUpdate';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 
-const log = logger('workflows');
-
 export const addUpdateAllNodesRequestedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: updateAllNodesRequested,
     effect: (action, { dispatch, getState }) => {
-      const nodes = selectNodes(getState());
+      const log = logger('nodes');
+      const { nodes } = getState().nodes.present;
       const templates = $templates.get();
 
       let unableToUpdateCount = 0;

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

@@ -1,6 +1,6 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { $nodeExecutionStates } from 'features/nodes/hooks/useExecutionState';
+import { parseify } from 'common/util/serialize';
 import { workflowLoaded, workflowLoadRequested } from 'features/nodes/store/actions';
 import { $templates } from 'features/nodes/store/nodesSlice';
 import { $needsFit } from 'features/nodes/store/reactFlowInstance';
@@ -10,14 +10,11 @@ import { graphToWorkflow } from 'features/nodes/util/workflow/graphToWorkflow';
 import { validateWorkflow } from 'features/nodes/util/workflow/validateWorkflow';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
-import { serializeError } from 'serialize-error';
 import { checkBoardAccess, checkImageAccess, checkModelAccess } from 'services/api/hooks/accessChecks';
 import type { GraphAndWorkflowResponse, NonNullableGraph } from 'services/api/types';
 import { z } from 'zod';
 import { fromZodError } from 'zod-validation-error';
 
-const log = logger('workflows');
-
 const getWorkflow = async (data: GraphAndWorkflowResponse, templates: Templates) => {
   if (data.workflow) {
     // Prefer to load the workflow if it's available - it has more information
@@ -37,6 +34,7 @@ export const addWorkflowLoadRequestedListener = (startAppListening: AppStartList
   startAppListening({
     actionCreator: workflowLoadRequested,
     effect: async (action, { dispatch }) => {
+      const log = logger('nodes');
       const { data, asCopy } = action.payload;
       const nodeTemplates = $templates.get();
 
@@ -48,7 +46,6 @@ export const addWorkflowLoadRequestedListener = (startAppListening: AppStartList
           delete workflow.id;
         }
 
-        $nodeExecutionStates.set({});
         dispatch(workflowLoaded(workflow));
         if (!warnings.length) {
           toast({
@@ -72,7 +69,7 @@ export const addWorkflowLoadRequestedListener = (startAppListening: AppStartList
       } catch (e) {
         if (e instanceof WorkflowVersionError) {
           // The workflow version was not recognized in the valid list of versions
-          log.error({ error: serializeError(e) }, e.message);
+          log.error({ error: parseify(e) }, e.message);
           toast({
             id: 'UNABLE_TO_VALIDATE_WORKFLOW',
             title: t('nodes.unableToValidateWorkflow'),
@@ -81,7 +78,7 @@ export const addWorkflowLoadRequestedListener = (startAppListening: AppStartList
           });
         } else if (e instanceof WorkflowMigrationError) {
           // There was a problem migrating the workflow to the latest version
-          log.error({ error: serializeError(e) }, e.message);
+          log.error({ error: parseify(e) }, e.message);
           toast({
             id: 'UNABLE_TO_VALIDATE_WORKFLOW',
             title: t('nodes.unableToValidateWorkflow'),
@@ -93,7 +90,7 @@ export const addWorkflowLoadRequestedListener = (startAppListening: AppStartList
           const { message } = fromZodError(e, {
             prefix: t('nodes.workflowValidation'),
           });
-          log.error({ error: serializeError(e) }, message);
+          log.error({ error: parseify(e) }, message);
           toast({
             id: 'UNABLE_TO_VALIDATE_WORKFLOW',
             title: t('nodes.unableToValidateWorkflow'),
@@ -102,7 +99,7 @@ export const addWorkflowLoadRequestedListener = (startAppListening: AppStartList
           });
         } else {
           // Some other error occurred
-          log.error({ error: serializeError(e) }, t('nodes.unknownErrorValidatingWorkflow'));
+          log.error({ error: parseify(e) }, t('nodes.unknownErrorValidatingWorkflow'));
           toast({
             id: 'UNABLE_TO_VALIDATE_WORKFLOW',
             title: t('nodes.unableToValidateWorkflow'),

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

@@ -1,5 +1,4 @@
-import { useStore } from '@nanostores/react';
-import type { AppStore } from 'app/store/store';
+import type { createStore } from 'app/store/store';
 import { atom } from 'nanostores';
 
 // Inject socket options and url into window for debugging
@@ -23,7 +22,7 @@ class ReduxStoreNotInitialized extends Error {
   }
 }
 
-export const $store = atom<Readonly<AppStore | undefined>>();
+export const $store = atom<Readonly<ReturnType<typeof createStore>> | undefined>();
 
 export const getStore = () => {
   const store = $store.get();
@@ -32,11 +31,3 @@ export const getStore = () => {
   }
   return store;
 };
-
-export const useAppStore = () => {
-  const store = useStore($store);
-  if (!store) {
-    throw new ReduxStoreNotInitialized();
-  }
-  return store;
-};