Merge remote-tracking branch 'origin/main' into bria-clone

* Add auto layout controls using elkjs to node editor

Introduces auto layout functionality for the node editor using elkjs, including a new UI popover for layout options (placement strategy, layering, spacing, direction). Adds related state and actions to workflowSettingsSlice, updates translations, and ensures elkjs is included in optimized dependencies.

* feat(nodes): Improve workflow auto-layout controls and accuracy

- The auto-layout settings panel is updated to use `Select` dropdowns and `NumberInput`
- The layout algorithm now uses the actual rendered dimensions of nodes from the DOM, falling back to estimates only when necessary. This results in a much more accurate and predictable layout.
- The ELKjs library integration is refactored to fix some warnings

* Update useAutoLayout.ts

prettier

* feat(nodes): Improve workflow auto-layout controls and accuracy

- The auto-layout settings panel is updated to use `Select` dropdowns and `NumberInput`
- The layout algorithm now uses the actual rendered dimensions of nodes from the DOM, falling back to estimates only when necessary. This results in a much more accurate and predictable layout.
- The ELKjs library integration is refactored to fix some warnings

* Update useAutoLayout.ts

prettier

* build(ui): import elkjs directly

* updated to use  dagrejs for autolayout

updated to use dagrejs - it has less layout options but is already included

but this is still WIP as some nodes don't report the height correctly. I am still investigating this...

* Update useAutoLayout.ts

update to fix layout issues

* minor updates

- pretty useAutoLayout.ts
- add missing type import in ViewportControls.tsx
- update pnpm-lock.yaml with elkjs removed

* Update ViewportControls.tsx

pnpm fix

* Fix Frontend check + single node selection fix

Fix Frontend check -  remove unused export from workflowSettingsSlice.ts
Update so that if you have a single node selected, it will auto layout all nodes, as this is a common thing to have a single node selected and means that you don't have to unselect it.

* feat(ui): misc improvements for autolayout

- Split popover into own component
- Add util functions to get node w/h
- Use magic wand icon for button
- Fix sizing of input components
- Use CompositeNumberInput instead of base chakra number input
- Add zod schemas for string values and use them in the component to
ensure state integrity

* chore(ui): lint

---------

Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -21,6 +21,20 @@ body:
         - label: I have searched the existing issues
           required: true
 
+  - type: dropdown
+    id: install_method
+    attributes:
+      label: Install method
+      description: How did you install Invoke?
+      multiple: false
+      options:
+        - "Invoke's Launcher"
+        - 'Stability Matrix'
+        - 'Pinokio'
+        - 'Manual'
+    validations:
+      required: true
+
   - type: markdown
     attributes:
       value: __Describe your environment__
@@ -76,8 +90,8 @@ body:
     attributes:
       label: Version number
       description: |
-        The version of Invoke you have installed. If it is not the latest version, please update and try again to confirm the issue still exists. If you are testing main, please include the commit hash instead.
-      placeholder: ex. 3.6.1
+        The version of Invoke you have installed. If it is not the [latest version](https://github.com/invoke-ai/InvokeAI/releases/latest), please update and try again to confirm the issue still exists. If you are testing main, please include the commit hash instead.
+      placeholder: ex. v6.0.2
     validations:
       required: true
 
@@ -85,17 +99,17 @@ body:
     id: browser-version
     attributes:
       label: Browser
-      description: Your web browser and version.
+      description: Your web browser and version, if you do not use the Launcher's provided GUI.
       placeholder: ex. Firefox 123.0b3
     validations:
-      required: true
+      required: false
 
   - type: textarea
     id: python-deps
     attributes:
-      label: Python dependencies
+      label: System Information
       description: |
-        If the problem occurred during image generation, click the gear icon at the bottom left corner, click "About", click the copy button and then paste here.
+        Click the gear icon at the bottom left corner, then click "About". Click the copy button and then paste here.
     validations:
       required: false
 

.gitignore

@@ -190,3 +190,5 @@ installer/update.bat
 installer/update.sh
 installer/InvokeAI-Installer/
 .aider*
+
+.claude/

docker/Dockerfile

@@ -5,8 +5,7 @@
 FROM docker.io/node:22-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
 ENV PATH="$PNPM_HOME:$PATH"
-RUN corepack use pnpm@8.x
-RUN corepack enable
+RUN corepack use pnpm@10.x && corepack enable
 
 WORKDIR /build
 COPY invokeai/frontend/web/ ./

docs/contributing/dev-environment.md

@@ -41,7 +41,7 @@ If you just want to use Invoke, you should use the [launcher][launcher link].
      With the modifications made, the install command should look something like this:
 
       ```sh
-      uv pip install -e ".[dev,test,docs,xformers]" --python 3.12 --python-preference only-managed --index=https://download.pytorch.org/whl/cu126 --reinstall
+      uv pip install -e ".[dev,test,docs,xformers]" --python 3.12 --python-preference only-managed --index=https://download.pytorch.org/whl/cu128 --reinstall
       ```
 
 6. At this point, you should have Invoke installed, a venv set up and activated, and the server running. But you will see a warning in the terminal that no UI was found. If you go to the URL for the server, you won't get a UI.
@@ -50,11 +50,11 @@ If you just want to use Invoke, you should use the [launcher][launcher link].
 
       If you only want to edit the docs, you can stop here and skip to the **Documentation** section below.
 
-7. Install the frontend dev toolchain:
+7. Install the frontend dev toolchain, paying attention to versions:
 
-      - [`nodejs`](https://nodejs.org/) (v20+)
+      - [`nodejs`](https://nodejs.org/) (tested on LTS, v22)
 
-      - [`pnpm`](https://pnpm.io/8.x/installation) (must be v8 - not v9!)
+      - [`pnpm`](https://pnpm.io/installation) (tested on v10)
 
 8. Do a production build of the frontend:
 

docs/contributing/frontend/workflows.md

@@ -297,7 +297,7 @@ Migration logic is in [migrations.ts].
 <!-- links -->
 
 [pydantic]: https://github.com/pydantic/pydantic 'pydantic'
-[zod]: https://github.com/colinhacks/zod 'zod/v4'
+[zod]: https://github.com/colinhacks/zod 'zod'
 [openapi-types]: https://github.com/kogosoftwarellc/open-api/tree/main/packages/openapi-types 'openapi-types'
 [reactflow]: https://github.com/xyflow/xyflow 'reactflow'
 [reactflow-concepts]: https://reactflow.dev/learn/concepts/terms-and-definitions

invokeai/app/invocations/bria_controlnet.py

@@ -0,0 +1,154 @@
+import cv2
+import numpy as np
+from PIL import Image
+from pydantic import BaseModel, Field
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    InputField,
+    OutputField,
+    UIType,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.bria.controlnet_aux.open_pose import Body, Face, Hand, OpenposeDetector
+from invokeai.backend.bria.controlnet_bria import BRIA_CONTROL_MODES
+from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
+from invokeai.invocation_api import Classification, ImageOutput
+
+DEPTH_SMALL_V2_URL = "depth-anything/Depth-Anything-V2-Small-hf"
+HF_LLLYASVIEL = "https://huggingface.co/lllyasviel/Annotators/resolve/main/"
+
+class BriaControlNetField(BaseModel):
+    image: ImageField = Field(description="The control image")
+    model: ModelIdentifierField = Field(description="The ControlNet model to use")
+    mode: BRIA_CONTROL_MODES = Field(description="The mode of the ControlNet")
+    conditioning_scale: float = Field(description="The weight given to the ControlNet")
+
+@invocation_output("bria_controlnet_output")
+class BriaControlNetOutput(BaseInvocationOutput):
+    """Bria ControlNet info"""
+
+    control: BriaControlNetField = OutputField(description=FieldDescriptions.control)
+    preprocessed_images: ImageField = OutputField(description="The preprocessed control image")
+
+
+@invocation(
+    "bria_controlnet",
+    title="ControlNet - Bria",
+    tags=["controlnet", "bria"],
+    category="controlnet",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaControlNetInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Collect Bria ControlNet info to pass to denoiser node."""
+
+    control_image: ImageField = InputField(description="The control image")
+    control_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.controlnet_model, ui_type=UIType.BriaControlNetModel
+    )
+    control_mode: BRIA_CONTROL_MODES = InputField(
+        default="depth", description="The mode of the ControlNet"
+    )
+    control_weight: float = InputField(
+        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
+    )
+
+    def invoke(self, context: InvocationContext) -> BriaControlNetOutput:
+        image_in = resize_img(context.images.get_pil(self.control_image.image_name))
+        if self.control_mode == "canny":
+            control_image = extract_canny(image_in)
+        elif self.control_mode == "depth":
+            control_image = extract_depth(image_in, context)
+        elif self.control_mode == "pose":
+            control_image = extract_openpose(image_in, context)
+        elif self.control_mode == "colorgrid":
+            control_image = tile(64, image_in)
+        elif self.control_mode == "recolor":
+            control_image = convert_to_grayscale(image_in)
+        elif self.control_mode == "tile":
+            control_image = tile(16, image_in)
+
+        control_image = resize_img(control_image)
+        image_dto = context.images.save(image=control_image)
+        image_output = ImageOutput.build(image_dto)
+        return BriaControlNetOutput(
+            preprocessed_images=image_output.image,
+            control=BriaControlNetField(
+                image=ImageField(image_name=image_dto.image_name),
+                model=self.control_model,
+                mode=self.control_mode,
+                conditioning_scale=self.control_weight,
+            ),
+        )
+
+
+RATIO_CONFIGS_1024 = {
+    0.6666666666666666: {"width": 832, "height": 1248},
+    0.7432432432432432: {"width": 880, "height": 1184},
+    0.8028169014084507: {"width": 912, "height": 1136},
+    1.0: {"width": 1024, "height": 1024},
+    1.2456140350877194: {"width": 1136, "height": 912},
+    1.3454545454545455: {"width": 1184, "height": 880},
+    1.4339622641509433: {"width": 1216, "height": 848},
+    1.5: {"width": 1248, "height": 832},
+    1.5490196078431373: {"width": 1264, "height": 816},
+    1.62: {"width": 1296, "height": 800},
+    1.7708333333333333: {"width": 1360, "height": 768},
+}
+
+def extract_depth(image: Image.Image, context: InvocationContext):
+    loaded_model = context.models.load_remote_model(DEPTH_SMALL_V2_URL, DepthAnythingPipeline.load_model)
+
+    with loaded_model as depth_anything_detector:
+        assert isinstance(depth_anything_detector, DepthAnythingPipeline)
+        depth_map = depth_anything_detector.generate_depth(image)
+    return depth_map
+
+def extract_openpose(image: Image.Image, context: InvocationContext):
+    body_model = context.models.load_remote_model(f"{HF_LLLYASVIEL}body_pose_model.pth", Body)
+    hand_model = context.models.load_remote_model(f"{HF_LLLYASVIEL}hand_pose_model.pth", Hand)
+    face_model = context.models.load_remote_model(f"{HF_LLLYASVIEL}facenet.pth", Face)
+
+    with body_model as body_model, hand_model as hand_model, face_model as face_model:
+        open_pose_model = OpenposeDetector(body_model, hand_model, face_model)
+        processed_image_open_pose = open_pose_model(image, hand_and_face=True)
+
+    processed_image_open_pose = processed_image_open_pose.resize(image.size)
+    return processed_image_open_pose
+
+
+def extract_canny(input_image):
+    image = np.array(input_image)
+    image = cv2.Canny(image, 100, 200)
+    image = image[:, :, None]
+    image = np.concatenate([image, image, image], axis=2)
+    canny_image = Image.fromarray(image)
+    return canny_image
+
+
+def convert_to_grayscale(image):
+    gray_image = image.convert('L').convert('RGB')
+    return gray_image
+
+def tile(downscale_factor, input_image):
+    control_image = input_image.resize((input_image.size[0] // downscale_factor, input_image.size[1] // downscale_factor)).resize(input_image.size, Image.Resampling.NEAREST)
+    return control_image
+
+def resize_img(control_image):
+    image_ratio = control_image.width / control_image.height
+    ratio = min(RATIO_CONFIGS_1024.keys(), key=lambda k: abs(k - image_ratio))
+    to_height = RATIO_CONFIGS_1024[ratio]["height"]
+    to_width = RATIO_CONFIGS_1024[ratio]["width"]
+    resized_image = control_image.resize((to_width, to_height), resample=Image.Resampling.LANCZOS)
+    return resized_image

invokeai/app/invocations/bria_decoder.py

@@ -0,0 +1,46 @@
+import torch
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from PIL import Image
+
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.invocations.primitives import FieldDescriptions, Input, InputField, LatentsField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.invocation_api import BaseInvocation, Classification, ImageOutput, invocation
+
+
+@invocation(
+    "bria_decoder",
+    title="Decoder - Bria",
+    tags=["image", "bria"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaDecoderInvocation(BaseInvocation):
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+        latents = latents.view(1, 64, 64, 4, 2, 2).permute(0, 3, 1, 4, 2, 5).reshape(1, 4, 128, 128)
+
+        with context.models.load(self.vae.vae) as vae:
+            assert isinstance(vae, AutoencoderKL)
+            latents = (latents / vae.config.scaling_factor)
+            latents = latents.to(device=vae.device, dtype=vae.dtype)
+
+            decoded_output = vae.decode(latents)
+            image = decoded_output.sample
+
+        # Convert to numpy with proper gradient handling
+        image = ((image.clamp(-1, 1) + 1) / 2 * 255).cpu().detach().permute(0, 2, 3, 1).numpy().astype("uint8")[0]
+        img = Image.fromarray(image)
+        image_dto = context.images.save(image=img)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/bria_denoiser.py

@@ -0,0 +1,185 @@
+from typing import List, Tuple
+
+import torch
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
+
+from invokeai.app.invocations.bria_controlnet import BriaControlNetField
+from invokeai.app.invocations.fields import Input, InputField, LatentsField, OutputField
+from invokeai.app.invocations.model import SubModelType, T5EncoderField, TransformerField, VAEField
+from invokeai.app.invocations.primitives import BaseInvocationOutput, FieldDescriptions
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.bria.controlnet_bria import BriaControlModes, BriaMultiControlNetModel
+from invokeai.backend.bria.controlnet_utils import prepare_control_images
+from invokeai.backend.bria.pipeline_bria_controlnet import BriaControlNetPipeline
+from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
+from invokeai.invocation_api import BaseInvocation, Classification, invocation, invocation_output
+
+
+@invocation_output("bria_denoise_output")
+class BriaDenoiseInvocationOutput(BaseInvocationOutput):
+    latents: LatentsField = OutputField(description=FieldDescriptions.latents)
+
+
+@invocation(
+    "bria_denoise",
+    title="Denoise - Bria",
+    tags=["image", "bria"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaDenoiseInvocation(BaseInvocation):
+    num_steps: int = InputField(
+        default=30, title="Number of Steps", description="The number of steps to use for the denoiser"
+    )
+    guidance_scale: float = InputField(
+        default=5.0, title="Guidance Scale", description="The guidance scale to use for the denoiser"
+    )
+
+    transformer: TransformerField = InputField(
+        description="Bria model (Transformer) to load",
+        input=Input.Connection,
+        title="Transformer",
+    )
+    t5_encoder: T5EncoderField = InputField(
+        title="T5Encoder",
+        description=FieldDescriptions.t5_encoder,
+        input=Input.Connection,
+    )
+    vae: VAEField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+        title="VAE",
+    )
+    latents: LatentsField = InputField(
+        description="Latents to denoise",
+        input=Input.Connection,
+        title="Latents",
+    )
+    latent_image_ids: LatentsField = InputField(
+        description="Latent Image IDs to denoise",
+        input=Input.Connection,
+        title="Latent Image IDs",
+    )
+    pos_embeds: LatentsField = InputField(
+        description="Positive Prompt Embeds",
+        input=Input.Connection,
+        title="Positive Prompt Embeds",
+    )
+    neg_embeds: LatentsField = InputField(
+        description="Negative Prompt Embeds",
+        input=Input.Connection,
+        title="Negative Prompt Embeds",
+    )
+    text_ids: LatentsField = InputField(
+        description="Text IDs",
+        input=Input.Connection,
+        title="Text IDs",
+    )
+    control: BriaControlNetField | list[BriaControlNetField] | None = InputField(
+        description="ControlNet",
+        input=Input.Connection,
+        title="ControlNet",
+        default = None,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> BriaDenoiseInvocationOutput:
+        latents = context.tensors.load(self.latents.latents_name)
+        pos_embeds = context.tensors.load(self.pos_embeds.latents_name)
+        neg_embeds = context.tensors.load(self.neg_embeds.latents_name)
+        text_ids = context.tensors.load(self.text_ids.latents_name)
+        latent_image_ids = context.tensors.load(self.latent_image_ids.latents_name)
+        scheduler_identifier = self.transformer.transformer.model_copy(update={"submodel_type": SubModelType.Scheduler})
+
+        device = None
+        dtype = None
+        with (
+            context.models.load(self.transformer.transformer) as transformer,
+            context.models.load(scheduler_identifier) as scheduler,
+            context.models.load(self.vae.vae) as vae,
+            context.models.load(self.t5_encoder.text_encoder) as t5_encoder,
+            context.models.load(self.t5_encoder.tokenizer) as t5_tokenizer,
+        ):
+            assert isinstance(transformer, BriaTransformer2DModel)
+            assert isinstance(scheduler, FlowMatchEulerDiscreteScheduler)
+            assert isinstance(vae, AutoencoderKL)
+            dtype = transformer.dtype
+            device = transformer.device
+            latents, pos_embeds, neg_embeds = (x.to(device, dtype) for x in (latents, pos_embeds, neg_embeds))
+
+            control_model, control_images, control_modes, control_scales = None, None, None, None
+            if self.control is not None:
+                control_model, control_images, control_modes, control_scales = self._prepare_multi_control(
+                    context=context,
+                    vae=vae,
+                    width=1024,
+                    height=1024,
+                    device=vae.device,
+                )
+
+            pipeline = BriaControlNetPipeline(
+                transformer=transformer,
+                scheduler=scheduler,
+                vae=vae,
+                text_encoder=t5_encoder,
+                tokenizer=t5_tokenizer,
+                controlnet=control_model,
+            )
+            pipeline.to(device=transformer.device, dtype=transformer.dtype)
+
+            latents = pipeline(
+                control_image=control_images,
+                control_mode=control_modes,
+                width=1024,
+                height=1024,
+                controlnet_conditioning_scale=control_scales,
+                num_inference_steps=self.num_steps,
+                max_sequence_length=128,
+                guidance_scale=self.guidance_scale,
+                latents=latents,
+                latent_image_ids=latent_image_ids,
+                text_ids=text_ids,
+                prompt_embeds=pos_embeds,
+                negative_prompt_embeds=neg_embeds,
+                output_type="latent",
+            )[0]
+
+        assert isinstance(latents, torch.Tensor)
+        saved_input_latents_tensor = context.tensors.save(latents)
+        latents_output = LatentsField(latents_name=saved_input_latents_tensor)
+        return BriaDenoiseInvocationOutput(latents=latents_output)
+
+
+    def _prepare_multi_control(
+        self,
+        context: InvocationContext,
+        vae: AutoencoderKL,
+        width: int,
+        height: int,
+        device: torch.device
+    ) -> Tuple[BriaMultiControlNetModel, List[torch.Tensor], List[torch.Tensor], List[float]]:
+
+        control = self.control if isinstance(self.control, list) else [self.control]
+        control_images, control_models, control_modes, control_scales = [], [], [], []
+        for controlnet in control:
+            if controlnet is not None:
+                control_models.append(context.models.load(controlnet.model).model)
+                control_modes.append(BriaControlModes[controlnet.mode].value)
+                control_scales.append(controlnet.conditioning_scale)
+                try:
+                    control_images.append(context.images.get_pil(controlnet.image.image_name))
+                except Exception:
+                    raise FileNotFoundError(f"Control image {controlnet.image.image_name} not found. Make sure not to delete the preprocessed image before finishing the pipeline.")
+
+        control_model = BriaMultiControlNetModel(control_models).to(device)
+        tensored_control_images, tensored_control_modes = prepare_control_images(
+            vae=vae,
+            control_images=control_images,
+            control_modes=control_modes,
+            width=width,
+            height=height,
+            device=device,
+            )
+        return control_model, tensored_control_images, tensored_control_modes, control_scales

invokeai/app/invocations/bria_latent_sampler.py

@@ -0,0 +1,76 @@
+import torch
+
+from invokeai.app.invocations.fields import Input, InputField, OutputField
+from invokeai.app.invocations.model import TransformerField
+from invokeai.app.invocations.primitives import (
+    BaseInvocationOutput,
+    FieldDescriptions,
+    LatentsField,
+)
+from invokeai.backend.bria.pipeline_bria_controlnet import prepare_latents
+from invokeai.invocation_api import (
+    BaseInvocation,
+    Classification,
+    InvocationContext,
+    invocation,
+    invocation_output,
+)
+
+
+@invocation_output("bria_latent_sampler_output")
+class BriaLatentSamplerInvocationOutput(BaseInvocationOutput):
+    """Base class for nodes that output a CogView text conditioning tensor."""
+
+    latents: LatentsField = OutputField(description=FieldDescriptions.cond)
+    latent_image_ids: LatentsField = OutputField(description=FieldDescriptions.cond)
+
+
+@invocation(
+    "bria_latent_sampler",
+    title="Latent Sampler - Bria",
+    tags=["image", "bria"],
+    category="image",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaLatentSamplerInvocation(BaseInvocation):
+    seed: int = InputField(
+        default=42,
+        title="Seed",
+        description="The seed to use for the latent sampler",
+    )
+    transformer: TransformerField = InputField(
+        description="Bria model (Transformer) to load",
+        input=Input.Connection,
+        title="Transformer",
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> BriaLatentSamplerInvocationOutput:
+        with context.models.load(self.transformer.transformer) as transformer:
+            device = transformer.device
+            dtype = transformer.dtype
+
+        height, width = 1024, 1024
+        generator = torch.Generator(device=device).manual_seed(self.seed)
+
+        num_channels_latents = 4
+        latents, latent_image_ids = prepare_latents(
+            batch_size=1,
+            num_channels_latents=num_channels_latents,
+            height=height,
+            width=width,
+            dtype=dtype,
+            device=device,
+            generator=generator,
+            )
+
+        saved_latents_tensor = context.tensors.save(latents)
+        saved_latent_image_ids_tensor = context.tensors.save(latent_image_ids)
+        latents_output = LatentsField(latents_name=saved_latents_tensor)
+        latent_image_ids_output = LatentsField(latents_name=saved_latent_image_ids_tensor)
+
+        return BriaLatentSamplerInvocationOutput(
+            latents=latents_output,
+            latent_image_ids=latent_image_ids_output,
+        )

invokeai/app/invocations/bria_model_loader.py

@@ -0,0 +1,58 @@
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.model import (
+    ModelIdentifierField,
+    SubModelType,
+    T5EncoderField,
+    TransformerField,
+    VAEField,
+)
+from invokeai.invocation_api import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    InvocationContext,
+    invocation,
+    invocation_output,
+)
+
+
+@invocation_output("bria_model_loader_output")
+class BriaModelLoaderOutput(BaseInvocationOutput):
+    """Bria base model loader output"""
+
+    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
+    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
+    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
+
+
+@invocation(
+    "bria_model_loader",
+    title="Main Model - Bria",
+    tags=["model", "bria"],
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaModelLoaderInvocation(BaseInvocation):
+    """Loads a bria base model, outputting its submodels."""
+
+    model: ModelIdentifierField = InputField(
+        description="Bria model (Transformer) to load",
+        ui_type=UIType.BriaMainModel,
+        input=Input.Direct,
+    )
+
+    def invoke(self, context: InvocationContext) -> BriaModelLoaderOutput:
+        for key in [self.model.key]:
+            if not context.models.exists(key):
+                raise ValueError(f"Unknown model: {key}")
+
+        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
+        text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
+        tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
+        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
+
+        return BriaModelLoaderOutput(
+            transformer=TransformerField(transformer=transformer, loras=[]),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[]),
+            vae=VAEField(vae=vae),
+        )

invokeai/app/invocations/bria_text_encoder.py

@@ -0,0 +1,93 @@
+from typing import Optional
+
+import torch
+from transformers import (
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+
+from invokeai.app.invocations.model import T5EncoderField
+from invokeai.app.invocations.primitives import BaseInvocationOutput, FieldDescriptions, Input, OutputField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.bria.pipeline_bria_controlnet import encode_prompt
+from invokeai.invocation_api import (
+    BaseInvocation,
+    Classification,
+    InputField,
+    LatentsField,
+    invocation,
+    invocation_output,
+)
+
+
+@invocation_output("bria_text_encoder_output")
+class BriaTextEncoderInvocationOutput(BaseInvocationOutput):
+    """Base class for nodes that output a CogView text conditioning tensor."""
+
+    pos_embeds: LatentsField = OutputField(description=FieldDescriptions.cond)
+    neg_embeds: LatentsField = OutputField(description=FieldDescriptions.cond)
+    text_ids: LatentsField = OutputField(description=FieldDescriptions.cond)
+
+
+@invocation(
+    "bria_text_encoder",
+    title="Prompt - Bria",
+    tags=["prompt", "conditioning", "bria"],
+    category="conditioning",
+    version="1.0.0",
+    classification=Classification.Prototype,
+)
+class BriaTextEncoderInvocation(BaseInvocation):
+    prompt: str = InputField(
+        title="Prompt",
+        description="The prompt to encode",
+    )
+    negative_prompt: Optional[str] = InputField(
+        title="Negative Prompt",
+        description="The negative prompt to encode",
+        default="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate",
+    )
+    max_length: int = InputField(
+        default=128,
+        title="Max Length",
+        description="The maximum length of the prompt",
+    )
+    t5_encoder: T5EncoderField = InputField(
+        title="T5Encoder",
+        description=FieldDescriptions.t5_encoder,
+        input=Input.Connection,
+    )
+
+    @torch.no_grad()
+    def invoke(self, context: InvocationContext) -> BriaTextEncoderInvocationOutput:
+        t5_encoder_info = context.models.load(self.t5_encoder.text_encoder)
+        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
+        with (
+            t5_encoder_info as text_encoder,
+            t5_tokenizer_info as tokenizer,
+        ):
+            assert isinstance(tokenizer, T5TokenizerFast)
+            assert isinstance(text_encoder, T5EncoderModel)
+
+        (prompt_embeds, negative_prompt_embeds, text_ids) = encode_prompt(
+            prompt=self.prompt,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            negative_prompt=self.negative_prompt,
+            device=text_encoder.device,
+            num_images_per_prompt=1,
+            max_sequence_length=self.max_length,
+            lora_scale=1.0,
+        )
+
+        saved_pos_tensor = context.tensors.save(prompt_embeds)
+        saved_neg_tensor = context.tensors.save(negative_prompt_embeds)
+        saved_text_ids_tensor = context.tensors.save(text_ids)
+        pos_embeds_output = LatentsField(latents_name=saved_pos_tensor)
+        neg_embeds_output = LatentsField(latents_name=saved_neg_tensor)
+        text_ids_output = LatentsField(latents_name=saved_text_ids_tensor)
+        return BriaTextEncoderInvocationOutput(
+            pos_embeds=pos_embeds_output,
+            neg_embeds=neg_embeds_output,
+            text_ids=text_ids_output,
+        )

invokeai/app/invocations/fields.py

@@ -42,6 +42,8 @@ class UIType(str, Enum, metaclass=MetaEnum):
     MainModel = "MainModelField"
     CogView4MainModel = "CogView4MainModelField"
     FluxMainModel = "FluxMainModelField"
+    BriaMainModel = "BriaMainModelField"
+    BriaControlNetModel = "BriaControlNetModelField"
     SD3MainModel = "SD3MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"

invokeai/app/invocations/primitives.py

@@ -430,6 +430,15 @@ class FluxConditioningOutput(BaseInvocationOutput):
         return cls(conditioning=FluxConditioningField(conditioning_name=conditioning_name))
 
 
+@invocation_output("flux_conditioning_collection_output")
+class FluxConditioningCollectionOutput(BaseInvocationOutput):
+    """Base class for nodes that output a collection of conditioning tensors"""
+
+    collection: list[FluxConditioningField] = OutputField(
+        description="The output conditioning tensors",
+    )
+
+
 @invocation_output("sd3_conditioning_output")
 class SD3ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single SD3 conditioning tensor"""

invokeai/backend/bria/bria_utils.py

@@ -0,0 +1,314 @@
+import math
+import os
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from diffusers.utils import logging
+from transformers import (
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def get_t5_prompt_embeds(
+    tokenizer: T5TokenizerFast,
+    text_encoder: T5EncoderModel,
+    prompt: Union[str, List[str], None] = None,
+    num_images_per_prompt: int = 1,
+    max_sequence_length: int = 128,
+    device: Optional[torch.device] = None,
+):
+    device = device or text_encoder.device
+
+    if prompt is None:
+        prompt = ""
+
+    prompt = [prompt] if isinstance(prompt, str) else prompt
+    batch_size = len(prompt)
+
+    text_inputs = tokenizer(
+        prompt,
+        # padding="max_length",
+        max_length=max_sequence_length,
+        truncation=True,
+        add_special_tokens=True,
+        return_tensors="pt",
+    )
+    text_input_ids = text_inputs.input_ids
+    untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+    if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+        removed_text = tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
+        logger.warning(
+            "The following part of your input was truncated because `max_sequence_length` is set to "
+            f" {max_sequence_length} tokens: {removed_text}"
+        )
+
+    prompt_embeds = text_encoder(text_input_ids.to(device))[0]
+
+    # Concat zeros to max_sequence
+    b, seq_len, dim = prompt_embeds.shape
+    if seq_len < max_sequence_length:
+        padding = torch.zeros(
+            (b, max_sequence_length - seq_len, dim), dtype=prompt_embeds.dtype, device=prompt_embeds.device
+        )
+        prompt_embeds = torch.concat([prompt_embeds, padding], dim=1)
+
+    prompt_embeds = prompt_embeds.to(device=device)
+
+    _, seq_len, _ = prompt_embeds.shape
+
+    # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+    prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+    return prompt_embeds
+
+
+# in order the get the same sigmas as in training and sample from them
+def get_original_sigmas(num_train_timesteps=1000, num_inference_steps=1000):
+    timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
+    sigmas = timesteps / num_train_timesteps
+
+    inds = [int(ind) for ind in np.linspace(0, num_train_timesteps - 1, num_inference_steps)]
+    new_sigmas = sigmas[inds]
+    return new_sigmas
+
+
+def is_ng_none(negative_prompt):
+    return (
+        negative_prompt is None
+        or negative_prompt == ""
+        or (isinstance(negative_prompt, list) and negative_prompt[0] is None)
+        or (isinstance(negative_prompt, list) and negative_prompt[0] == "")
+    )
+
+
+class CudaTimerContext:
+    def __init__(self, times_arr):
+        self.times_arr = times_arr
+
+    def __enter__(self):
+        self.before_event = torch.cuda.Event(enable_timing=True)
+        self.after_event = torch.cuda.Event(enable_timing=True)
+        self.before_event.record()
+
+    def __exit__(self, type, value, traceback):
+        self.after_event.record()
+        torch.cuda.synchronize()
+        elapsed_time = self.before_event.elapsed_time(self.after_event) / 1000
+        self.times_arr.append(elapsed_time)
+
+
+def get_env_prefix():
+    env = os.environ.get("CLOUD_PROVIDER", "AWS").upper()
+    if env == "AWS":
+        return "SM_CHANNEL"
+    elif env == "AZURE":
+        return "AZUREML_DATAREFERENCE"
+
+    raise Exception(f"Env {env} not supported")
+
+
+def compute_density_for_timestep_sampling(
+    weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None
+):
+    """Compute the density for sampling the timesteps when doing SD3 training.
+
+    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
+
+    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    """
+    if weighting_scheme == "logit_normal":
+        # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
+        u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu")
+        u = torch.nn.functional.sigmoid(u)
+    elif weighting_scheme == "mode":
+        u = torch.rand(size=(batch_size,), device="cpu")
+        u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
+    else:
+        u = torch.rand(size=(batch_size,), device="cpu")
+    return u
+
+
+def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
+    """Computes loss weighting scheme for SD3 training.
+
+    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
+
+    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    """
+    if weighting_scheme == "sigma_sqrt":
+        weighting = (sigmas**-2.0).float()
+    elif weighting_scheme == "cosmap":
+        bot = 1 - 2 * sigmas + 2 * sigmas**2
+        weighting = 2 / (math.pi * bot)
+    else:
+        weighting = torch.ones_like(sigmas)
+    return weighting
+
+
+def initialize_distributed():
+    # Initialize the process group for distributed training
+    dist.init_process_group("nccl")
+
+    # Get the current process's rank (ID) and the total number of processes (world size)
+    rank = dist.get_rank()
+    world_size = dist.get_world_size()
+
+    print(f"Initialized distributed training: Rank {rank}/{world_size}")
+
+
+def get_clip_prompt_embeds(
+    text_encoder: CLIPTextModel,
+    text_encoder_2: CLIPTextModelWithProjection,
+    tokenizer: CLIPTokenizer,
+    tokenizer_2: CLIPTokenizer,
+    prompt: Union[str, List[str]] = None,
+    num_images_per_prompt: int = 1,
+    max_sequence_length: int = 77,
+    device: Optional[torch.device] = None,
+):
+    device = device or text_encoder.device
+    assert max_sequence_length == tokenizer.model_max_length
+    prompt = [prompt] if isinstance(prompt, str) else prompt
+
+    # Define tokenizers and text encoders
+    tokenizers = [tokenizer, tokenizer_2]
+    text_encoders = [text_encoder, text_encoder_2]
+
+    # textual inversion: process multi-vector tokens if necessary
+    prompt_embeds_list = []
+    prompts = [prompt, prompt]
+    for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders, strict=False):
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids
+        prompt_embeds = text_encoder(text_input_ids.to(text_encoder.device), output_hidden_states=True)
+
+        # We are only ALWAYS interested in the pooled output of the final text encoder
+        pooled_prompt_embeds = prompt_embeds[0]
+        prompt_embeds = prompt_embeds.hidden_states[-2]
+
+        prompt_embeds_list.append(prompt_embeds)
+
+    prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+    bs_embed, seq_len, _ = prompt_embeds.shape
+    # duplicate text embeddings for each generation per prompt, using mps friendly method
+    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+    prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+    pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+        bs_embed * num_images_per_prompt, -1
+    )
+
+    return prompt_embeds, pooled_prompt_embeds
+
+
+def get_1d_rotary_pos_embed(
+    dim: int,
+    pos: Union[np.ndarray, int],
+    theta: float = 10000.0,
+    use_real=False,
+    linear_factor=1.0,
+    ntk_factor=1.0,
+    repeat_interleave_real=True,
+    freqs_dtype=torch.float32,  #  torch.float32, torch.float64 (flux)
+):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
+    index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
+    data type.
+
+    Args:
+        dim (`int`): Dimension of the frequency tensor.
+        pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
+        theta (`float`, *optional*, defaults to 10000.0):
+            Scaling factor for frequency computation. Defaults to 10000.0.
+        use_real (`bool`, *optional*):
+            If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+        linear_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor for the context extrapolation. Defaults to 1.0.
+        ntk_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor for the NTK-Aware RoPE. Defaults to 1.0.
+        repeat_interleave_real (`bool`, *optional*, defaults to `True`):
+            If `True` and `use_real`, real part and imaginary part are each interleaved with themselves to reach `dim`.
+            Otherwise, they are concateanted with themselves.
+        freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`):
+            the dtype of the frequency tensor.
+    Returns:
+        `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
+    """
+    assert dim % 2 == 0
+
+    if isinstance(pos, int):
+        pos = torch.arange(pos)
+    if isinstance(pos, np.ndarray):
+        pos = torch.from_numpy(pos)  # type: ignore  # [S]
+
+    theta = theta * ntk_factor
+    freqs = (
+        1.0
+        / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=pos.device)[: (dim // 2)] / dim))
+        / linear_factor
+    )  # [D/2]
+    freqs = torch.outer(pos, freqs)  # type: ignore   # [S, D/2]
+    if use_real and repeat_interleave_real:
+        # flux, hunyuan-dit, cogvideox
+        freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
+        freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float()  # [S, D]
+        return freqs_cos, freqs_sin
+    elif use_real:
+        # stable audio, allegro
+        freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float()  # [S, D]
+        freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float()  # [S, D]
+        return freqs_cos, freqs_sin
+    else:
+        # lumina
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
+        return freqs_cis
+
+
+class FluxPosEmbed(torch.nn.Module):
+    # modified from https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/modules/layers.py#L11
+    def __init__(self, theta: int, axes_dim: List[int]):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: torch.Tensor) -> torch.Tensor:
+        n_axes = ids.shape[-1]
+        cos_out = []
+        sin_out = []
+        pos = ids.float()
+        is_mps = ids.device.type == "mps"
+        freqs_dtype = torch.float32 if is_mps else torch.float64
+        for i in range(n_axes):
+            cos, sin = get_1d_rotary_pos_embed(
+                self.axes_dim[i],
+                pos[:, i],
+                theta=self.theta,
+                repeat_interleave_real=True,
+                use_real=True,
+                freqs_dtype=freqs_dtype,
+            )
+            cos_out.append(cos)
+            sin_out.append(sin)
+        freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
+        freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
+        return freqs_cos, freqs_sin

invokeai/backend/bria/controlnet_aux/__init__.py

@@ -0,0 +1,6 @@
+__version__ = "0.0.9"
+
+from invokeai.backend.bria.controlnet_aux.canny import CannyDetector as CannyDetector
+from invokeai.backend.bria.controlnet_aux.open_pose import OpenposeDetector as OpenposeDetector
+
+__all__ = ["CannyDetector", "OpenposeDetector"]

invokeai/backend/bria/controlnet_aux/canny/__init__.py

@@ -0,0 +1,39 @@
+import warnings
+
+import cv2
+import numpy as np
+from PIL import Image
+
+from invokeai.backend.bria.controlnet_aux.util import HWC3, resize_image
+
+
+class CannyDetector:
+    def __call__(self, input_image=None, low_threshold=100, high_threshold=200, detect_resolution=512, image_resolution=512, output_type=None, **kwargs):
+        if "img" in kwargs:
+            warnings.warn("img is deprecated, please use `input_image=...` instead.", DeprecationWarning, stacklevel=2)
+            input_image = kwargs.pop("img")
+
+        if input_image is None:
+            raise ValueError("input_image must be defined.")
+
+        if not isinstance(input_image, np.ndarray):
+            input_image = np.array(input_image, dtype=np.uint8)
+            output_type = output_type or "pil"
+        else:
+            output_type = output_type or "np"
+
+        input_image = HWC3(input_image)
+        input_image = resize_image(input_image, detect_resolution)
+
+        detected_map = cv2.Canny(input_image, low_threshold, high_threshold)
+        detected_map = HWC3(detected_map)
+
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
+
+        if output_type == "pil":
+            detected_map = Image.fromarray(detected_map)
+
+        return detected_map

invokeai/backend/bria/controlnet_aux/open_pose/LICENSE

@@ -0,0 +1,108 @@
+OPENPOSE: MULTIPERSON KEYPOINT DETECTION
+SOFTWARE LICENSE AGREEMENT
+ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
+
+BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
+
+This is a license agreement ("Agreement") between your academic institution or non-profit organization or self (called "Licensee" or "You" in this Agreement) and Carnegie Mellon University (called "Licensor" in this Agreement).  All rights not specifically granted to you in this Agreement are reserved for Licensor. 
+
+RESERVATION OF OWNERSHIP AND GRANT OF LICENSE: 
+Licensor retains exclusive ownership of any copy of the Software (as defined below) licensed under this Agreement and hereby grants to Licensee a personal, non-exclusive, 
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invokeai/backend/bria/controlnet_aux/open_pose/__init__.py

@@ -0,0 +1,233 @@
+# Openpose
+# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
+# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
+# 3rd Edited by ControlNet
+# 4th Edited by ControlNet (added face and correct hands)
+# 5th Edited by ControlNet (Improved JSON serialization/deserialization, and lots of bug fixs)
+# This preprocessor is licensed by CMU for non-commercial use only.
+
+
+import os
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+import warnings
+from typing import List, NamedTuple, Tuple, Union
+
+import cv2
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from invokeai.backend.bria.controlnet_aux.open_pose import util
+from invokeai.backend.bria.controlnet_aux.open_pose.body import Body, BodyResult, Keypoint
+from invokeai.backend.bria.controlnet_aux.open_pose.face import Face
+from invokeai.backend.bria.controlnet_aux.open_pose.hand import Hand
+from invokeai.backend.bria.controlnet_aux.util import HWC3, resize_image
+
+HandResult = List[Keypoint]
+FaceResult = List[Keypoint]
+
+class PoseResult(NamedTuple):
+    body: BodyResult
+    left_hand: Union[HandResult, None]
+    right_hand: Union[HandResult, None]
+    face: Union[FaceResult, None]
+
+def draw_poses(poses: List[PoseResult], H, W, draw_body=True, draw_hand=True, draw_face=True):
+    """
+    Draw the detected poses on an empty canvas.
+
+    Args:
+        poses (List[PoseResult]): A list of PoseResult objects containing the detected poses.
+        H (int): The height of the canvas.
+        W (int): The width of the canvas.
+        draw_body (bool, optional): Whether to draw body keypoints. Defaults to True.
+        draw_hand (bool, optional): Whether to draw hand keypoints. Defaults to True.
+        draw_face (bool, optional): Whether to draw face keypoints. Defaults to True.
+
+    Returns:
+        numpy.ndarray: A 3D numpy array representing the canvas with the drawn poses.
+    """
+    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
+
+    for pose in poses:
+        if draw_body:
+            canvas = util.draw_bodypose(canvas, pose.body.keypoints)
+
+        if draw_hand:
+            canvas = util.draw_handpose(canvas, pose.left_hand)
+            canvas = util.draw_handpose(canvas, pose.right_hand)
+
+        if draw_face:
+            canvas = util.draw_facepose(canvas, pose.face)
+
+    return canvas
+
+
+class OpenposeDetector:
+    """
+    A class for detecting human poses in images using the Openpose model.
+
+    Attributes:
+        model_dir (str): Path to the directory where the pose models are stored.
+    """
+    def __init__(self, body_estimation, hand_estimation=None, face_estimation=None):
+        self.body_estimation = body_estimation
+        self.hand_estimation = hand_estimation
+        self.face_estimation = face_estimation
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path, filename=None, hand_filename=None, face_filename=None, cache_dir=None, local_files_only=False):
+
+        if pretrained_model_or_path == "lllyasviel/ControlNet":
+            filename = filename or "annotator/ckpts/body_pose_model.pth"
+            hand_filename = hand_filename or "annotator/ckpts/hand_pose_model.pth"
+            face_filename = face_filename or "facenet.pth"
+
+            face_pretrained_model_or_path = "lllyasviel/Annotators"
+        else:
+            filename = filename or "body_pose_model.pth"
+            hand_filename = hand_filename or "hand_pose_model.pth"
+            face_filename = face_filename or "facenet.pth"
+
+            face_pretrained_model_or_path = pretrained_model_or_path
+
+        if os.path.isdir(pretrained_model_or_path):
+            body_model_path = os.path.join(pretrained_model_or_path, filename)
+            hand_model_path = os.path.join(pretrained_model_or_path, hand_filename)
+            face_model_path = os.path.join(face_pretrained_model_or_path, face_filename)
+        else:
+            body_model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)
+            hand_model_path = hf_hub_download(pretrained_model_or_path, hand_filename, cache_dir=cache_dir, local_files_only=local_files_only)
+            face_model_path = hf_hub_download(face_pretrained_model_or_path, face_filename, cache_dir=cache_dir, local_files_only=local_files_only)
+
+        body_estimation = Body(body_model_path)
+        hand_estimation = Hand(hand_model_path)
+        face_estimation = Face(face_model_path)
+
+        return cls(body_estimation, hand_estimation, face_estimation)
+
+    def to(self, device):
+        self.body_estimation.to(device)
+        self.hand_estimation.to(device)
+        self.face_estimation.to(device)
+        return self
+
+    def detect_hands(self, body: BodyResult, oriImg) -> Tuple[Union[HandResult, None], Union[HandResult, None]]:
+        left_hand = None
+        right_hand = None
+        H, W, _ = oriImg.shape
+        for x, y, w, is_left in util.handDetect(body, oriImg):
+            peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :]).astype(np.float32)
+            if peaks.ndim == 2 and peaks.shape[1] == 2:
+                peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
+                peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
+
+                hand_result = [
+                    Keypoint(x=peak[0], y=peak[1])
+                    for peak in peaks
+                ]
+
+                if is_left:
+                    left_hand = hand_result
+                else:
+                    right_hand = hand_result
+
+        return left_hand, right_hand
+
+    def detect_face(self, body: BodyResult, oriImg) -> Union[FaceResult, None]:
+        face = util.faceDetect(body, oriImg)
+        if face is None:
+            return None
+
+        x, y, w = face
+        H, W, _ = oriImg.shape
+        heatmaps = self.face_estimation(oriImg[y:y+w, x:x+w, :])
+        peaks = self.face_estimation.compute_peaks_from_heatmaps(heatmaps).astype(np.float32)
+        if peaks.ndim == 2 and peaks.shape[1] == 2:
+            peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
+            peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
+            return [
+                Keypoint(x=peak[0], y=peak[1])
+                for peak in peaks
+            ]
+
+        return None
+
+    def detect_poses(self, oriImg, include_hand=False, include_face=False) -> List[PoseResult]:
+        """
+        Detect poses in the given image.
+            Args:
+                oriImg (numpy.ndarray): The input image for pose detection.
+                include_hand (bool, optional): Whether to include hand detection. Defaults to False.
+                include_face (bool, optional): Whether to include face detection. Defaults to False.
+
+        Returns:
+            List[PoseResult]: A list of PoseResult objects containing the detected poses.
+        """
+        oriImg = oriImg[:, :, ::-1].copy()
+        H, W, C = oriImg.shape
+        with torch.no_grad():
+            candidate, subset = self.body_estimation(oriImg)
+            bodies = self.body_estimation.format_body_result(candidate, subset)
+
+            results = []
+            for body in bodies:
+                left_hand, right_hand, face = (None,) * 3
+                if include_hand:
+                    left_hand, right_hand = self.detect_hands(body, oriImg)
+                if include_face:
+                    face = self.detect_face(body, oriImg)
+
+                results.append(PoseResult(BodyResult(
+                    keypoints=[
+                        Keypoint(
+                            x=keypoint.x / float(W),
+                            y=keypoint.y / float(H)
+                        ) if keypoint is not None else None
+                        for keypoint in body.keypoints
+                    ],
+                    total_score=body.total_score,
+                    total_parts=body.total_parts
+                ), left_hand, right_hand, face))
+
+            return results
+
+    def __call__(self, input_image, detect_resolution=512, image_resolution=512, include_body=True, include_hand=False, include_face=False, hand_and_face=None, output_type="pil", **kwargs):
+        if hand_and_face is not None:
+            warnings.warn("hand_and_face is deprecated. Use include_hand and include_face instead.", DeprecationWarning, stacklevel=2)
+            include_hand = hand_and_face
+            include_face = hand_and_face
+
+        if "return_pil" in kwargs:
+            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning, stacklevel=2)
+            output_type = "pil" if kwargs["return_pil"] else "np"
+        if type(output_type) is bool:
+            warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions", stacklevel=2)
+            if output_type:
+                output_type = "pil"
+
+        if not isinstance(input_image, np.ndarray):
+            input_image = np.array(input_image, dtype=np.uint8)
+
+        input_image = HWC3(input_image)
+        input_image = resize_image(input_image, detect_resolution)
+        H, W, C = input_image.shape
+
+        poses = self.detect_poses(input_image, include_hand, include_face)
+        canvas = draw_poses(poses, H, W, draw_body=include_body, draw_hand=include_hand, draw_face=include_face)
+
+        detected_map = canvas
+        detected_map = HWC3(detected_map)
+
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
+
+        if output_type == "pil":
+            detected_map = Image.fromarray(detected_map)
+
+        return detected_map

invokeai/backend/bria/controlnet_aux/open_pose/body.py

@@ -0,0 +1,259 @@
+import math
+from typing import List, NamedTuple, Union
+
+import numpy as np
+import torch
+from scipy.ndimage.filters import gaussian_filter
+
+from invokeai.backend.bria.controlnet_aux.open_pose import util
+from invokeai.backend.bria.controlnet_aux.open_pose.model import bodypose_model
+
+
+class Keypoint(NamedTuple):
+    x: float
+    y: float
+    score: float = 1.0
+    id: int = -1
+
+
+class BodyResult(NamedTuple):
+    # Note: Using `Union` instead of `|` operator as the ladder is a Python
+    # 3.10 feature.
+    # Annotator code should be Python 3.8 Compatible, as controlnet repo uses
+    # Python 3.8 environment.
+    # https://github.com/lllyasviel/ControlNet/blob/d3284fcd0972c510635a4f5abe2eeb71dc0de524/environment.yaml#L6
+    keypoints: List[Union[Keypoint, None]]
+    total_score: float
+    total_parts: int
+
+
+class Body(object):
+    def __init__(self, model_path):
+        self.model = bodypose_model()
+        model_dict = util.transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, oriImg):
+        device = next(iter(self.model.parameters())).device
+        # scale_search = [0.5, 1.0, 1.5, 2.0]
+        scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre1 = 0.1
+        thre2 = 0.05
+        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
+        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
+        paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = util.smart_resize_k(oriImg, fx=scale, fy=scale)
+            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
+            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            data = data.to(device)
+            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
+            with torch.no_grad():
+                Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
+            Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
+            Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
+
+            # extract outputs, resize, and remove padding
+            # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride)
+            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            heatmap = util.smart_resize(heatmap, (oriImg.shape[0], oriImg.shape[1]))
+
+            # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
+            paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0))  # output 0 is PAFs
+            paf = util.smart_resize_k(paf, fx=stride, fy=stride)
+            paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            paf = util.smart_resize(paf, (oriImg.shape[0], oriImg.shape[1]))
+
+            heatmap_avg += heatmap_avg + heatmap / len(multiplier)
+            paf_avg += + paf / len(multiplier)
+
+        all_peaks = []
+        peak_counter = 0
+
+        for part in range(18):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+
+            map_left = np.zeros(one_heatmap.shape)
+            map_left[1:, :] = one_heatmap[:-1, :]
+            map_right = np.zeros(one_heatmap.shape)
+            map_right[:-1, :] = one_heatmap[1:, :]
+            map_up = np.zeros(one_heatmap.shape)
+            map_up[:, 1:] = one_heatmap[:, :-1]
+            map_down = np.zeros(one_heatmap.shape)
+            map_down[:, :-1] = one_heatmap[:, 1:]
+
+            peaks_binary = np.logical_and.reduce(
+                (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1))
+            peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0], strict=False))  # note reverse
+            peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
+            peak_id = range(peak_counter, peak_counter + len(peaks))
+            peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
+
+            all_peaks.append(peaks_with_score_and_id)
+            peak_counter += len(peaks)
+
+        # find connection in the specified sequence, center 29 is in the position 15
+        limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
+                   [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
+                   [1, 16], [16, 18], [3, 17], [6, 18]]
+        # the middle joints heatmap correpondence
+        mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
+                  [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
+                  [55, 56], [37, 38], [45, 46]]
+
+        connection_all = []
+        special_k = []
+        mid_num = 10
+
+        for k in range(len(mapIdx)):
+            score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
+            candA = all_peaks[limbSeq[k][0] - 1]
+            candB = all_peaks[limbSeq[k][1] - 1]
+            nA = len(candA)
+            nB = len(candB)
+            indexA, indexB = limbSeq[k]
+            if (nA != 0 and nB != 0):
+                connection_candidate = []
+                for i in range(nA):
+                    for j in range(nB):
+                        vec = np.subtract(candB[j][:2], candA[i][:2])
+                        norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
+                        norm = max(0.001, norm)
+                        vec = np.divide(vec, norm)
+
+                        startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
+                                            np.linspace(candA[i][1], candB[j][1], num=mid_num), strict=False))
+
+                        vec_x = np.array([score_mid[int(round(startend[i][1])), int(round(startend[i][0])), 0] \
+                                          for i in range(len(startend))])
+                        vec_y = np.array([score_mid[int(round(startend[i][1])), int(round(startend[i][0])), 1] \
+                                          for i in range(len(startend))])
+
+                        score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
+                        score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
+                            0.5 * oriImg.shape[0] / norm - 1, 0)
+                        criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
+                        criterion2 = score_with_dist_prior > 0
+                        if criterion1 and criterion2:
+                            connection_candidate.append(
+                                [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]])
+
+                connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
+                connection = np.zeros((0, 5))
+                for c in range(len(connection_candidate)):
+                    i, j, s = connection_candidate[c][0:3]
+                    if (i not in connection[:, 3] and j not in connection[:, 4]):
+                        connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
+                        if (len(connection) >= min(nA, nB)):
+                            break
+
+                connection_all.append(connection)
+            else:
+                special_k.append(k)
+                connection_all.append([])
+
+        # last number in each row is the total parts number of that person
+        # the second last number in each row is the score of the overall configuration
+        subset = -1 * np.ones((0, 20))
+        candidate = np.array([item for sublist in all_peaks for item in sublist])
+
+        for k in range(len(mapIdx)):
+            if k not in special_k:
+                partAs = connection_all[k][:, 0]
+                partBs = connection_all[k][:, 1]
+                indexA, indexB = np.array(limbSeq[k]) - 1
+
+                for i in range(len(connection_all[k])):  # = 1:size(temp,1)
+                    found = 0
+                    subset_idx = [-1, -1]
+                    for j in range(len(subset)):  # 1:size(subset,1):
+                        if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
+                            subset_idx[found] = j
+                            found += 1
+
+                    if found == 1:
+                        j = subset_idx[0]
+                        if subset[j][indexB] != partBs[i]:
+                            subset[j][indexB] = partBs[i]
+                            subset[j][-1] += 1
+                            subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+                    elif found == 2:  # if found 2 and disjoint, merge them
+                        j1, j2 = subset_idx
+                        membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
+                        if len(np.nonzero(membership == 2)[0]) == 0:  # merge
+                            subset[j1][:-2] += (subset[j2][:-2] + 1)
+                            subset[j1][-2:] += subset[j2][-2:]
+                            subset[j1][-2] += connection_all[k][i][2]
+                            subset = np.delete(subset, j2, 0)
+                        else:  # as like found == 1
+                            subset[j1][indexB] = partBs[i]
+                            subset[j1][-1] += 1
+                            subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+
+                    # if find no partA in the subset, create a new subset
+                    elif not found and k < 17:
+                        row = -1 * np.ones(20)
+                        row[indexA] = partAs[i]
+                        row[indexB] = partBs[i]
+                        row[-1] = 2
+                        row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
+                        subset = np.vstack([subset, row])
+        # delete some rows of subset which has few parts occur
+        deleteIdx = []
+        for i in range(len(subset)):
+            if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
+                deleteIdx.append(i)
+        subset = np.delete(subset, deleteIdx, axis=0)
+
+        # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
+        # candidate: x, y, score, id
+        return candidate, subset
+
+    @staticmethod
+    def format_body_result(candidate: np.ndarray, subset: np.ndarray) -> List[BodyResult]:
+        """
+        Format the body results from the candidate and subset arrays into a list of BodyResult objects.
+
+        Args:
+            candidate (np.ndarray): An array of candidates containing the x, y coordinates, score, and id
+                for each body part.
+            subset (np.ndarray): An array of subsets containing indices to the candidate array for each
+                person detected. The last two columns of each row hold the total score and total parts
+                of the person.
+
+        Returns:
+            List[BodyResult]: A list of BodyResult objects, where each object represents a person with
+                detected keypoints, total score, and total parts.
+        """
+        return [
+            BodyResult(
+                keypoints=[
+                    Keypoint(
+                        x=candidate[candidate_index][0],
+                        y=candidate[candidate_index][1],
+                        score=candidate[candidate_index][2],
+                        id=candidate[candidate_index][3]
+                    ) if candidate_index != -1 else None
+                    for candidate_index in person[:18].astype(int)
+                ],
+                total_score=person[18],
+                total_parts=person[19]
+            )
+            for person in subset
+        ]

invokeai/backend/bria/controlnet_aux/open_pose/face.py

@@ -0,0 +1,364 @@
+import logging
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.nn import Conv2d, MaxPool2d, Module, ReLU, init
+from torchvision.transforms import ToPILImage, ToTensor
+
+from invokeai.backend.bria.controlnet_aux.open_pose import util
+
+
+class FaceNet(Module):
+    """Model the cascading heatmaps. """
+    def __init__(self):
+        super(FaceNet, self).__init__()
+        # cnn to make feature map
+        self.relu = ReLU()
+        self.max_pooling_2d = MaxPool2d(kernel_size=2, stride=2)
+        self.conv1_1 = Conv2d(in_channels=3, out_channels=64,
+                              kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = Conv2d(
+            in_channels=64, out_channels=64, kernel_size=3, stride=1,
+            padding=1)
+        self.conv2_1 = Conv2d(
+            in_channels=64, out_channels=128, kernel_size=3, stride=1,
+            padding=1)
+        self.conv2_2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_1 = Conv2d(
+            in_channels=128, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_2 = Conv2d(
+            in_channels=256, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_3 = Conv2d(
+            in_channels=256, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_4 = Conv2d(
+            in_channels=256, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_1 = Conv2d(
+            in_channels=256, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_2 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_3 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_4 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv5_1 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv5_2 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv5_3_CPM = Conv2d(
+            in_channels=512, out_channels=128, kernel_size=3, stride=1,
+            padding=1)
+
+        # stage1
+        self.conv6_1_CPM = Conv2d(
+            in_channels=128, out_channels=512, kernel_size=1, stride=1,
+            padding=0)
+        self.conv6_2_CPM = Conv2d(
+            in_channels=512, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage2
+        self.Mconv1_stage2 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage2 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage3
+        self.Mconv1_stage3 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage3 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage4
+        self.Mconv1_stage4 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage4 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage5
+        self.Mconv1_stage5 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage5 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage6
+        self.Mconv1_stage6 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage6 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        for m in self.modules():
+            if isinstance(m, Conv2d):
+                init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        """Return a list of heatmaps."""
+        heatmaps = []
+
+        h = self.relu(self.conv1_1(x))
+        h = self.relu(self.conv1_2(h))
+        h = self.max_pooling_2d(h)
+        h = self.relu(self.conv2_1(h))
+        h = self.relu(self.conv2_2(h))
+        h = self.max_pooling_2d(h)
+        h = self.relu(self.conv3_1(h))
+        h = self.relu(self.conv3_2(h))
+        h = self.relu(self.conv3_3(h))
+        h = self.relu(self.conv3_4(h))
+        h = self.max_pooling_2d(h)
+        h = self.relu(self.conv4_1(h))
+        h = self.relu(self.conv4_2(h))
+        h = self.relu(self.conv4_3(h))
+        h = self.relu(self.conv4_4(h))
+        h = self.relu(self.conv5_1(h))
+        h = self.relu(self.conv5_2(h))
+        h = self.relu(self.conv5_3_CPM(h))
+        feature_map = h
+
+        # stage1
+        h = self.relu(self.conv6_1_CPM(h))
+        h = self.conv6_2_CPM(h)
+        heatmaps.append(h)
+
+        # stage2
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage2(h))
+        h = self.relu(self.Mconv2_stage2(h))
+        h = self.relu(self.Mconv3_stage2(h))
+        h = self.relu(self.Mconv4_stage2(h))
+        h = self.relu(self.Mconv5_stage2(h))
+        h = self.relu(self.Mconv6_stage2(h))
+        h = self.Mconv7_stage2(h)
+        heatmaps.append(h)
+
+        # stage3
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage3(h))
+        h = self.relu(self.Mconv2_stage3(h))
+        h = self.relu(self.Mconv3_stage3(h))
+        h = self.relu(self.Mconv4_stage3(h))
+        h = self.relu(self.Mconv5_stage3(h))
+        h = self.relu(self.Mconv6_stage3(h))
+        h = self.Mconv7_stage3(h)
+        heatmaps.append(h)
+
+        # stage4
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage4(h))
+        h = self.relu(self.Mconv2_stage4(h))
+        h = self.relu(self.Mconv3_stage4(h))
+        h = self.relu(self.Mconv4_stage4(h))
+        h = self.relu(self.Mconv5_stage4(h))
+        h = self.relu(self.Mconv6_stage4(h))
+        h = self.Mconv7_stage4(h)
+        heatmaps.append(h)
+
+        # stage5
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage5(h))
+        h = self.relu(self.Mconv2_stage5(h))
+        h = self.relu(self.Mconv3_stage5(h))
+        h = self.relu(self.Mconv4_stage5(h))
+        h = self.relu(self.Mconv5_stage5(h))
+        h = self.relu(self.Mconv6_stage5(h))
+        h = self.Mconv7_stage5(h)
+        heatmaps.append(h)
+
+        # stage6
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage6(h))
+        h = self.relu(self.Mconv2_stage6(h))
+        h = self.relu(self.Mconv3_stage6(h))
+        h = self.relu(self.Mconv4_stage6(h))
+        h = self.relu(self.Mconv5_stage6(h))
+        h = self.relu(self.Mconv6_stage6(h))
+        h = self.Mconv7_stage6(h)
+        heatmaps.append(h)
+
+        return heatmaps
+
+
+LOG = logging.getLogger(__name__)
+TOTEN = ToTensor()
+TOPIL = ToPILImage()
+
+
+params = {
+    'gaussian_sigma': 2.5,
+    'inference_img_size': 736,  # 368, 736, 1312
+    'heatmap_peak_thresh': 0.1,
+    'crop_scale': 1.5,
+    'line_indices': [
+        [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6],
+        [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13],
+        [13, 14], [14, 15], [15, 16],
+        [17, 18], [18, 19], [19, 20], [20, 21],
+        [22, 23], [23, 24], [24, 25], [25, 26],
+        [27, 28], [28, 29], [29, 30],
+        [31, 32], [32, 33], [33, 34], [34, 35],
+        [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 36],
+        [42, 43], [43, 44], [44, 45], [45, 46], [46, 47], [47, 42],
+        [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54],
+        [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 48],
+        [60, 61], [61, 62], [62, 63], [63, 64], [64, 65], [65, 66],
+        [66, 67], [67, 60]
+    ],
+}
+
+
+class Face(object):
+    """
+    The OpenPose face landmark detector model.
+
+    Args:
+        inference_size: set the size of the inference image size, suggested:
+            368, 736, 1312, default 736
+        gaussian_sigma: blur the heatmaps, default 2.5
+        heatmap_peak_thresh: return landmark if over threshold, default 0.1
+
+    """
+    def __init__(self, face_model_path,
+                 inference_size=None,
+                 gaussian_sigma=None,
+                 heatmap_peak_thresh=None):
+        self.inference_size = inference_size or params["inference_img_size"]
+        self.sigma = gaussian_sigma or params['gaussian_sigma']
+        self.threshold = heatmap_peak_thresh or params["heatmap_peak_thresh"]
+        self.model = FaceNet()
+        self.model.load_state_dict(torch.load(face_model_path))
+        self.model.eval()
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, face_img):
+        device = next(iter(self.model.parameters())).device
+        H, W, C = face_img.shape
+
+        w_size = 384
+        x_data = torch.from_numpy(util.smart_resize(face_img, (w_size, w_size))).permute([2, 0, 1]) / 256.0 - 0.5
+
+        x_data = x_data.to(device)
+
+        with torch.no_grad():
+            hs = self.model(x_data[None, ...])
+            heatmaps = F.interpolate(
+                hs[-1],
+                (H, W),
+                mode='bilinear', align_corners=True).cpu().numpy()[0]
+        return heatmaps
+
+    def compute_peaks_from_heatmaps(self, heatmaps):
+        all_peaks = []
+        for part in range(heatmaps.shape[0]):
+            map_ori = heatmaps[part].copy()
+            binary = np.ascontiguousarray(map_ori > 0.05, dtype=np.uint8)
+
+            if np.sum(binary) == 0:
+                continue
+
+            positions = np.where(binary > 0.5)
+            intensities = map_ori[positions]
+            mi = np.argmax(intensities)
+            y, x = positions[0][mi], positions[1][mi]
+            all_peaks.append([x, y])
+
+        return np.array(all_peaks)

invokeai/backend/bria/controlnet_aux/open_pose/hand.py

@@ -0,0 +1,90 @@
+import cv2
+import numpy as np
+import torch
+from scipy.ndimage.filters import gaussian_filter
+from skimage.measure import label
+
+from invokeai.backend.bria.controlnet_aux.open_pose import util
+from invokeai.backend.bria.controlnet_aux.open_pose.model import handpose_model
+
+
+class Hand(object):
+    def __init__(self, model_path):
+        self.model = handpose_model()
+        model_dict = util.transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, oriImgRaw):
+        device = next(iter(self.model.parameters())).device
+        scale_search = [0.5, 1.0, 1.5, 2.0]
+        # scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre = 0.05
+        multiplier = [x * boxsize for x in scale_search]
+
+        wsize = 128
+        heatmap_avg = np.zeros((wsize, wsize, 22))
+
+        Hr, Wr, Cr = oriImgRaw.shape
+
+        oriImg = cv2.GaussianBlur(oriImgRaw, (0, 0), 0.8)
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = util.smart_resize(oriImg, (scale, scale))
+
+            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
+            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            data = data.to(device)
+
+            with torch.no_grad():
+                output = self.model(data).cpu().numpy()
+
+            # extract outputs, resize, and remove padding
+            heatmap = np.transpose(np.squeeze(output), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride)
+            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            heatmap = util.smart_resize(heatmap, (wsize, wsize))
+
+            heatmap_avg += heatmap / len(multiplier)
+
+        all_peaks = []
+        for part in range(21):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+            binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
+
+            if np.sum(binary) == 0:
+                all_peaks.append([0, 0])
+                continue
+            label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
+            max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
+            label_img[label_img != max_index] = 0
+            map_ori[label_img == 0] = 0
+
+            y, x = util.npmax(map_ori)
+            y = int(float(y) * float(Hr) / float(wsize))
+            x = int(float(x) * float(Wr) / float(wsize))
+            all_peaks.append([x, y])
+        return np.array(all_peaks)
+
+if __name__ == "__main__":
+    hand_estimation = Hand('../model/hand_pose_model.pth')
+
+    # test_image = '../images/hand.jpg'
+    test_image = '../images/hand.jpg'
+    oriImg = cv2.imread(test_image)  # B,G,R order
+    peaks = hand_estimation(oriImg)
+    canvas = util.draw_handpose(oriImg, peaks, True)
+    cv2.imshow('', canvas)
+    cv2.waitKey(0)

invokeai/backend/bria/controlnet_aux/open_pose/model.py

@@ -0,0 +1,217 @@
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+
+def make_layers(block, no_relu_layers):
+    layers = []
+    for layer_name, v in block.items():
+        if 'pool' in layer_name:
+            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
+                                    padding=v[2])
+            layers.append((layer_name, layer))
+        else:
+            conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
+                               kernel_size=v[2], stride=v[3],
+                               padding=v[4])
+            layers.append((layer_name, conv2d))
+            if layer_name not in no_relu_layers:
+                layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))
+
+    return nn.Sequential(OrderedDict(layers))
+
+class bodypose_model(nn.Module):
+    def __init__(self):
+        super(bodypose_model, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
+                          'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
+                          'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
+                          'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
+        blocks = {}
+        block0 = OrderedDict([
+                      ('conv1_1', [3, 64, 3, 1, 1]),
+                      ('conv1_2', [64, 64, 3, 1, 1]),
+                      ('pool1_stage1', [2, 2, 0]),
+                      ('conv2_1', [64, 128, 3, 1, 1]),
+                      ('conv2_2', [128, 128, 3, 1, 1]),
+                      ('pool2_stage1', [2, 2, 0]),
+                      ('conv3_1', [128, 256, 3, 1, 1]),
+                      ('conv3_2', [256, 256, 3, 1, 1]),
+                      ('conv3_3', [256, 256, 3, 1, 1]),
+                      ('conv3_4', [256, 256, 3, 1, 1]),
+                      ('pool3_stage1', [2, 2, 0]),
+                      ('conv4_1', [256, 512, 3, 1, 1]),
+                      ('conv4_2', [512, 512, 3, 1, 1]),
+                      ('conv4_3_CPM', [512, 256, 3, 1, 1]),
+                      ('conv4_4_CPM', [256, 128, 3, 1, 1])
+                  ])
+
+
+        # Stage 1
+        block1_1 = OrderedDict([
+                        ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
+                        ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
+                    ])
+
+        block1_2 = OrderedDict([
+                        ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
+                        ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
+                    ])
+        blocks['block1_1'] = block1_1
+        blocks['block1_2'] = block1_2
+
+        self.model0 = make_layers(block0, no_relu_layers)
+
+        # Stages 2 - 6
+        for i in range(2, 7):
+            blocks['block%d_1' % i] = OrderedDict([
+                    ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
+                ])
+
+            blocks['block%d_2' % i] = OrderedDict([
+                    ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
+                ])
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_1 = blocks['block1_1']
+        self.model2_1 = blocks['block2_1']
+        self.model3_1 = blocks['block3_1']
+        self.model4_1 = blocks['block4_1']
+        self.model5_1 = blocks['block5_1']
+        self.model6_1 = blocks['block6_1']
+
+        self.model1_2 = blocks['block1_2']
+        self.model2_2 = blocks['block2_2']
+        self.model3_2 = blocks['block3_2']
+        self.model4_2 = blocks['block4_2']
+        self.model5_2 = blocks['block5_2']
+        self.model6_2 = blocks['block6_2']
+
+
+    def forward(self, x):
+
+        out1 = self.model0(x)
+
+        out1_1 = self.model1_1(out1)
+        out1_2 = self.model1_2(out1)
+        out2 = torch.cat([out1_1, out1_2, out1], 1)
+
+        out2_1 = self.model2_1(out2)
+        out2_2 = self.model2_2(out2)
+        out3 = torch.cat([out2_1, out2_2, out1], 1)
+
+        out3_1 = self.model3_1(out3)
+        out3_2 = self.model3_2(out3)
+        out4 = torch.cat([out3_1, out3_2, out1], 1)
+
+        out4_1 = self.model4_1(out4)
+        out4_2 = self.model4_2(out4)
+        out5 = torch.cat([out4_1, out4_2, out1], 1)
+
+        out5_1 = self.model5_1(out5)
+        out5_2 = self.model5_2(out5)
+        out6 = torch.cat([out5_1, out5_2, out1], 1)
+
+        out6_1 = self.model6_1(out6)
+        out6_2 = self.model6_2(out6)
+
+        return out6_1, out6_2
+
+class handpose_model(nn.Module):
+    def __init__(self):
+        super(handpose_model, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
+                          'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
+        # stage 1
+        block1_0 = OrderedDict([
+                ('conv1_1', [3, 64, 3, 1, 1]),
+                ('conv1_2', [64, 64, 3, 1, 1]),
+                ('pool1_stage1', [2, 2, 0]),
+                ('conv2_1', [64, 128, 3, 1, 1]),
+                ('conv2_2', [128, 128, 3, 1, 1]),
+                ('pool2_stage1', [2, 2, 0]),
+                ('conv3_1', [128, 256, 3, 1, 1]),
+                ('conv3_2', [256, 256, 3, 1, 1]),
+                ('conv3_3', [256, 256, 3, 1, 1]),
+                ('conv3_4', [256, 256, 3, 1, 1]),
+                ('pool3_stage1', [2, 2, 0]),
+                ('conv4_1', [256, 512, 3, 1, 1]),
+                ('conv4_2', [512, 512, 3, 1, 1]),
+                ('conv4_3', [512, 512, 3, 1, 1]),
+                ('conv4_4', [512, 512, 3, 1, 1]),
+                ('conv5_1', [512, 512, 3, 1, 1]),
+                ('conv5_2', [512, 512, 3, 1, 1]),
+                ('conv5_3_CPM', [512, 128, 3, 1, 1])
+            ])
+
+        block1_1 = OrderedDict([
+            ('conv6_1_CPM', [128, 512, 1, 1, 0]),
+            ('conv6_2_CPM', [512, 22, 1, 1, 0])
+        ])
+
+        blocks = {}
+        blocks['block1_0'] = block1_0
+        blocks['block1_1'] = block1_1
+
+        # stage 2-6
+        for i in range(2, 7):
+            blocks['block%d' % i] = OrderedDict([
+                    ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
+                ])
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_0 = blocks['block1_0']
+        self.model1_1 = blocks['block1_1']
+        self.model2 = blocks['block2']
+        self.model3 = blocks['block3']
+        self.model4 = blocks['block4']
+        self.model5 = blocks['block5']
+        self.model6 = blocks['block6']
+
+    def forward(self, x):
+        out1_0 = self.model1_0(x)
+        out1_1 = self.model1_1(out1_0)
+        concat_stage2 = torch.cat([out1_1, out1_0], 1)
+        out_stage2 = self.model2(concat_stage2)
+        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
+        out_stage3 = self.model3(concat_stage3)
+        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
+        out_stage4 = self.model4(concat_stage4)
+        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
+        out_stage5 = self.model5(concat_stage5)
+        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
+        out_stage6 = self.model6(concat_stage6)
+        return out_stage6

invokeai/backend/bria/controlnet_aux/open_pose/util.py

@@ -0,0 +1,388 @@
+import math
+from typing import List, Tuple, Union
+
+import cv2
+import numpy as np
+
+from invokeai.backend.bria.controlnet_aux.open_pose.body import BodyResult, Keypoint
+
+eps = 0.01
+
+
+def smart_resize(x, s):
+    Ht, Wt = s
+    if x.ndim == 2:
+        Ho, Wo = x.shape
+        Co = 1
+    else:
+        Ho, Wo, Co = x.shape
+    if Co == 3 or Co == 1:
+        k = float(Ht + Wt) / float(Ho + Wo)
+        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
+    else:
+        return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2)
+
+
+def smart_resize_k(x, fx, fy):
+    if x.ndim == 2:
+        Ho, Wo = x.shape
+        Co = 1
+    else:
+        Ho, Wo, Co = x.shape
+    Ht, Wt = Ho * fy, Wo * fx
+    if Co == 3 or Co == 1:
+        k = float(Ht + Wt) / float(Ho + Wo)
+        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
+    else:
+        return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2)
+
+
+def padRightDownCorner(img, stride, padValue):
+    h = img.shape[0]
+    w = img.shape[1]
+
+    pad = 4 * [None]
+    pad[0] = 0 # up
+    pad[1] = 0 # left
+    pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
+    pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
+
+    img_padded = img
+    pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))
+    img_padded = np.concatenate((pad_up, img_padded), axis=0)
+    pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))
+    img_padded = np.concatenate((pad_left, img_padded), axis=1)
+    pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))
+    img_padded = np.concatenate((img_padded, pad_down), axis=0)
+    pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))
+    img_padded = np.concatenate((img_padded, pad_right), axis=1)
+
+    return img_padded, pad
+
+
+def transfer(model, model_weights):
+    transfered_model_weights = {}
+    for weights_name in model.state_dict().keys():
+        transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]
+    return transfered_model_weights
+
+
+def draw_bodypose(canvas: np.ndarray, keypoints: List[Keypoint]) -> np.ndarray:
+    """
+    Draw keypoints and limbs representing body pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the body pose.
+        keypoints (List[Keypoint]): A list of Keypoint objects representing the body keypoints to be drawn.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn body pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    H, W, C = canvas.shape
+    stickwidth = 4
+
+    limbSeq = [
+        [2, 3], [2, 6], [3, 4], [4, 5],
+        [6, 7], [7, 8], [2, 9], [9, 10],
+        [10, 11], [2, 12], [12, 13], [13, 14],
+        [2, 1], [1, 15], [15, 17], [1, 16],
+        [16, 18],
+    ]
+
+    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
+              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
+              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
+
+    for (k1_index, k2_index), color in zip(limbSeq, colors, strict=False):
+        keypoint1 = keypoints[k1_index - 1]
+        keypoint2 = keypoints[k2_index - 1]
+
+        if keypoint1 is None or keypoint2 is None:
+            continue
+
+        Y = np.array([keypoint1.x, keypoint2.x]) * float(W)
+        X = np.array([keypoint1.y, keypoint2.y]) * float(H)
+        mX = np.mean(X)
+        mY = np.mean(Y)
+        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+        cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color])
+
+    for keypoint, color in zip(keypoints, colors, strict=False):
+        if keypoint is None:
+            continue
+
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1)
+
+    return canvas
+
+
+def draw_handpose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray:
+    import matplotlib
+    """
+    Draw keypoints and connections representing hand pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    if not keypoints:
+        return canvas
+
+    H, W, C = canvas.shape
+
+    edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \
+             [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
+
+    for ie, (e1, e2) in enumerate(edges):
+        k1 = keypoints[e1]
+        k2 = keypoints[e2]
+        if k1 is None or k2 is None:
+            continue
+
+        x1 = int(k1.x * W)
+        y1 = int(k1.y * H)
+        x2 = int(k2.x * W)
+        y2 = int(k2.y * H)
+        if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+            cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=2)
+
+    for keypoint in keypoints:
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        if x > eps and y > eps:
+            cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+    return canvas
+
+
+def draw_facepose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray:
+    """
+    Draw keypoints representing face pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the face pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the face keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn face pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    if not keypoints:
+        return canvas
+
+    H, W, C = canvas.shape
+    for keypoint in keypoints:
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        if x > eps and y > eps:
+            cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
+    return canvas
+
+
+# detect hand according to body pose keypoints
+# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
+def handDetect(body: BodyResult, oriImg) -> List[Tuple[int, int, int, bool]]:
+    """
+    Detect hands in the input body pose keypoints and calculate the bounding box for each hand.
+
+    Args:
+        body (BodyResult): A BodyResult object containing the detected body pose keypoints.
+        oriImg (numpy.ndarray): A 3D numpy array representing the original input image.
+
+    Returns:
+        List[Tuple[int, int, int, bool]]: A list of tuples, each containing the coordinates (x, y) of the top-left
+                                          corner of the bounding box, the width (height) of the bounding box, and
+                                          a boolean flag indicating whether the hand is a left hand (True) or a
+                                          right hand (False).
+
+    Notes:
+        - The width and height of the bounding boxes are equal since the network requires squared input.
+        - The minimum bounding box size is 20 pixels.
+    """
+    ratioWristElbow = 0.33
+    detect_result = []
+    image_height, image_width = oriImg.shape[0:2]
+
+    keypoints = body.keypoints
+    # right hand: wrist 4, elbow 3, shoulder 2
+    # left hand: wrist 7, elbow 6, shoulder 5
+    left_shoulder = keypoints[5]
+    left_elbow = keypoints[6]
+    left_wrist = keypoints[7]
+    right_shoulder = keypoints[2]
+    right_elbow = keypoints[3]
+    right_wrist = keypoints[4]
+
+    # if any of three not detected
+    has_left = all(keypoint is not None for keypoint in (left_shoulder, left_elbow, left_wrist))
+    has_right = all(keypoint is not None for keypoint in (right_shoulder, right_elbow, right_wrist))
+    if not (has_left or has_right):
+        return []
+
+    hands = []
+    #left hand
+    if has_left:
+        hands.append([
+            left_shoulder.x, left_shoulder.y,
+            left_elbow.x, left_elbow.y,
+            left_wrist.x, left_wrist.y,
+            True
+        ])
+    # right hand
+    if has_right:
+        hands.append([
+            right_shoulder.x, right_shoulder.y,
+            right_elbow.x, right_elbow.y,
+            right_wrist.x, right_wrist.y,
+            False
+        ])
+
+    for x1, y1, x2, y2, x3, y3, is_left in hands:
+        # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
+        # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
+        # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
+        # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
+        # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
+        # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
+        x = x3 + ratioWristElbow * (x3 - x2)
+        y = y3 + ratioWristElbow * (y3 - y2)
+        distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
+        distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+        width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
+        # x-y refers to the center --> offset to topLeft point
+        # handRectangle.x -= handRectangle.width / 2.f;
+        # handRectangle.y -= handRectangle.height / 2.f;
+        x -= width / 2
+        y -= width / 2  # width = height
+        # overflow the image
+        if x < 0:
+            x = 0
+        if y < 0:
+            y = 0
+        width1 = width
+        width2 = width
+        if x + width > image_width:
+            width1 = image_width - x
+        if y + width > image_height:
+            width2 = image_height - y
+        width = min(width1, width2)
+        # the max hand box value is 20 pixels
+        if width >= 20:
+            detect_result.append((int(x), int(y), int(width), is_left))
+
+    '''
+    return value: [[x, y, w, True if left hand else False]].
+    width=height since the network require squared input.
+    x, y is the coordinate of top left.
+    '''
+    return detect_result
+
+
+# Written by Lvmin
+def faceDetect(body: BodyResult, oriImg) -> Union[Tuple[int, int, int], None]:
+    """
+    Detect the face in the input body pose keypoints and calculate the bounding box for the face.
+
+    Args:
+        body (BodyResult): A BodyResult object containing the detected body pose keypoints.
+        oriImg (numpy.ndarray): A 3D numpy array representing the original input image.
+
+    Returns:
+        Tuple[int, int, int] | None: A tuple containing the coordinates (x, y) of the top-left corner of the
+                                   bounding box and the width (height) of the bounding box, or None if the
+                                   face is not detected or the bounding box width is less than 20 pixels.
+
+    Notes:
+        - The width and height of the bounding box are equal.
+        - The minimum bounding box size is 20 pixels.
+    """
+    # left right eye ear 14 15 16 17
+    image_height, image_width = oriImg.shape[0:2]
+
+    keypoints = body.keypoints
+    head = keypoints[0]
+    left_eye = keypoints[14]
+    right_eye = keypoints[15]
+    left_ear = keypoints[16]
+    right_ear = keypoints[17]
+
+    if head is None or all(keypoint is None for keypoint in (left_eye, right_eye, left_ear, right_ear)):
+        return None
+
+    width = 0.0
+    x0, y0 = head.x, head.y
+
+    if left_eye is not None:
+        x1, y1 = left_eye.x, left_eye.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 3.0)
+
+    if right_eye is not None:
+        x1, y1 = right_eye.x, right_eye.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 3.0)
+
+    if left_ear is not None:
+        x1, y1 = left_ear.x, left_ear.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 1.5)
+
+    if right_ear is not None:
+        x1, y1 = right_ear.x, right_ear.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 1.5)
+
+    x, y = x0, y0
+
+    x -= width
+    y -= width
+
+    if x < 0:
+        x = 0
+
+    if y < 0:
+        y = 0
+
+    width1 = width * 2
+    width2 = width * 2
+
+    if x + width > image_width:
+        width1 = image_width - x
+
+    if y + width > image_height:
+        width2 = image_height - y
+
+    width = min(width1, width2)
+
+    if width >= 20:
+        return int(x), int(y), int(width)
+    else:
+        return None
+
+
+# get max index of 2d array
+def npmax(array):
+    arrayindex = array.argmax(1)
+    arrayvalue = array.max(1)
+    i = arrayvalue.argmax()
+    j = arrayindex[i]
+    return i, j

invokeai/backend/bria/controlnet_aux/util.py

@@ -0,0 +1,146 @@
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+
+annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts')
+
+
+def HWC3(x):
+    assert x.dtype == np.uint8
+    if x.ndim == 2:
+        x = x[:, :, None]
+    assert x.ndim == 3
+    H, W, C = x.shape
+    assert C == 1 or C == 3 or C == 4
+    if C == 3:
+        return x
+    if C == 1:
+        return np.concatenate([x, x, x], axis=2)
+    if C == 4:
+        color = x[:, :, 0:3].astype(np.float32)
+        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
+        y = color * alpha + 255.0 * (1.0 - alpha)
+        y = y.clip(0, 255).astype(np.uint8)
+        return y
+
+
+def make_noise_disk(H, W, C, F):
+    noise = np.random.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C))
+    noise = cv2.resize(noise, (W + 2 * F, H + 2 * F), interpolation=cv2.INTER_CUBIC)
+    noise = noise[F: F + H, F: F + W]
+    noise -= np.min(noise)
+    noise /= np.max(noise)
+    if C == 1:
+        noise = noise[:, :, None]
+    return noise
+
+
+def nms(x, t, s):
+    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
+
+    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
+    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
+    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
+    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
+
+    y = np.zeros_like(x)
+
+    for f in [f1, f2, f3, f4]:
+        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
+
+    z = np.zeros_like(y, dtype=np.uint8)
+    z[y > t] = 255
+    return z
+
+def min_max_norm(x):
+    x -= np.min(x)
+    x /= np.maximum(np.max(x), 1e-5)
+    return x
+
+
+def safe_step(x, step=2):
+    y = x.astype(np.float32) * float(step + 1)
+    y = y.astype(np.int32).astype(np.float32) / float(step)
+    return y
+
+
+def img2mask(img, H, W, low=10, high=90):
+    assert img.ndim == 3 or img.ndim == 2
+    assert img.dtype == np.uint8
+
+    if img.ndim == 3:
+        y = img[:, :, random.randrange(0, img.shape[2])]
+    else:
+        y = img
+
+    y = cv2.resize(y, (W, H), interpolation=cv2.INTER_CUBIC)
+
+    if random.uniform(0, 1) < 0.5:
+        y = 255 - y
+
+    return y < np.percentile(y, random.randrange(low, high))
+
+
+def resize_image(input_image, resolution):
+    H, W, C = input_image.shape
+    H = float(H)
+    W = float(W)
+    k = float(resolution) / min(H, W)
+    H *= k
+    W *= k
+    H = int(np.round(H / 64.0)) * 64
+    W = int(np.round(W / 64.0)) * 64
+    img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
+    return img
+
+
+def torch_gc():
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+
+def ade_palette():
+    """ADE20K palette that maps each class to RGB values."""
+    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
+            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
+            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
+            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
+            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
+            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
+            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
+            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
+            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
+            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
+            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
+            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
+            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
+            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
+            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
+            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
+            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
+            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
+            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
+            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
+            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
+            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
+            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
+            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
+            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
+            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
+            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
+            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
+            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
+            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
+            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
+            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
+            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
+            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
+            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
+            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
+            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
+            [102, 255, 0], [92, 0, 255]]
+

invokeai/backend/bria/controlnet_bria.py

@@ -0,0 +1,547 @@
+# type: ignore
+# Copyright 2024 Black Forest Labs, The HuggingFace Team and The InstantX Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Dict, List, Literal, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import PeftAdapterMixin
+from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.controlnet import zero_module
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.utils.outputs import BaseOutput
+
+from invokeai.backend.bria.transformer_bria import (
+    EmbedND,
+    FluxSingleTransformerBlock,
+    FluxTransformerBlock,
+    TimestepProjEmbeddings,
+)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+BRIA_CONTROL_MODES = Literal["depth", "canny", "colorgrid", "recolor", "tile", "pose"]
+class BriaControlModes(Enum):
+    depth = 0
+    canny = 1
+    colorgrid = 2
+    recolor = 3
+    tile = 4
+    pose = 5
+
+
+@dataclass
+class BriaControlNetOutput(BaseOutput):
+    controlnet_block_samples: Tuple[torch.Tensor]
+    controlnet_single_block_samples: Tuple[torch.Tensor]
+
+
+class BriaControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 1,
+        in_channels: int = 64,
+        num_layers: int = 19,
+        num_single_layers: int = 38,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 4096,
+        pooled_projection_dim: int = 768,
+        guidance_embeds: bool = False,
+        axes_dims_rope: Optional[List[int]] = None,
+        num_mode: int = None,
+        rope_theta: int = 10000,
+        time_theta: int = 10000,
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        # self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
+        axes_dims_rope = [16, 56, 56] if axes_dims_rope is None else axes_dims_rope
+        self.pos_embed = EmbedND(theta=rope_theta, axes_dim=axes_dims_rope)
+
+        # text_time_guidance_cls = (
+        # CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
+        # )
+        # self.time_text_embed = text_time_guidance_cls(
+        # embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
+        # )
+        self.time_embed = TimestepProjEmbeddings(
+            embedding_dim=self.inner_dim, time_theta=time_theta
+        )
+
+        self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim)
+        self.x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                FluxTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                FluxSingleTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                )
+                for i in range(num_single_layers)
+            ]
+        )
+
+        # controlnet_blocks
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(len(self.transformer_blocks)):
+            self.controlnet_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
+
+        self.controlnet_single_blocks = nn.ModuleList([])
+        for _ in range(len(self.single_transformer_blocks)):
+            self.controlnet_single_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
+
+        self.union = num_mode is not None and num_mode > 0
+        if self.union:
+            self.controlnet_mode_embedder = nn.Embedding(num_mode, self.inner_dim)
+
+        self.controlnet_x_embedder = zero_module(torch.nn.Linear(in_channels, self.inner_dim))
+
+        self.gradient_checkpointing = False
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self):
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(self, processor):
+        r"""
+        Sets the attention processor to use to compute attention.
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    @classmethod
+    def from_transformer(
+        cls,
+        transformer,
+        num_layers: int = 4,
+        num_single_layers: int = 10,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        load_weights_from_transformer=True,
+    ):
+        config = transformer.config
+        config["num_layers"] = num_layers
+        config["num_single_layers"] = num_single_layers
+        config["attention_head_dim"] = attention_head_dim
+        config["num_attention_heads"] = num_attention_heads
+
+        controlnet = cls(**config)
+
+        if load_weights_from_transformer:
+            controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
+            controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
+            controlnet.context_embedder.load_state_dict(transformer.context_embedder.state_dict())
+            controlnet.x_embedder.load_state_dict(transformer.x_embedder.state_dict())
+            controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
+            controlnet.single_transformer_blocks.load_state_dict(
+                transformer.single_transformer_blocks.state_dict(), strict=False
+            )
+
+            controlnet.controlnet_x_embedder = zero_module(controlnet.controlnet_x_embedder)
+
+        return controlnet
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        controlnet_cond: torch.Tensor,
+        controlnet_mode: torch.Tensor = None,
+        conditioning_scale: float = 1.0,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+        """
+        The [`FluxTransformer2DModel`] forward method.
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input `hidden_states`.
+            controlnet_cond (`torch.Tensor`):
+                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
+            controlnet_mode (`torch.Tensor`):
+                The mode tensor of shape `(batch_size, 1)`.
+            conditioning_scale (`float`, defaults to `1.0`):
+                The scale factor for ControlNet outputs.
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+                from the embeddings of input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+                A list of tensors that if specified are added to the residuals of transformer blocks.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if guidance is not None:
+            print("guidance is not supported in BriaControlNetModel")
+        if pooled_projections is not None:
+            print("pooled_projections is not supported in BriaControlNetModel")
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+        hidden_states = self.x_embedder(hidden_states)
+
+        # Convert controlnet_cond to the same dtype as the model weights
+        controlnet_cond = controlnet_cond.to(dtype=self.controlnet_x_embedder.weight.dtype)
+
+        # add
+        hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_cond)
+
+        timestep = timestep.to(hidden_states.dtype)  # Original code was * 1000
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype)  # Original code was * 1000
+        else:
+            guidance = None
+
+        temb = self.time_embed(timestep, dtype=hidden_states.dtype)
+
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if txt_ids.ndim == 3:
+            logger.warning(
+                "Passing `txt_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            txt_ids = txt_ids[0]
+        if img_ids.ndim == 3:
+            logger.warning(
+                "Passing `img_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            img_ids = img_ids[0]
+
+        if self.union:
+            # union mode
+            if controlnet_mode is None:
+                raise ValueError("`controlnet_mode` cannot be `None` when applying ControlNet-Union")
+
+            # Validate controlnet_mode values are within the valid range
+            if torch.any(controlnet_mode < 0) or torch.any(controlnet_mode >= self.num_mode):
+                raise ValueError(f"`controlnet_mode` values must be in range [0, {self.num_mode-1}], but got values outside this range")
+
+            # union mode emb
+            controlnet_mode_emb = self.controlnet_mode_embedder(controlnet_mode)
+            if controlnet_mode_emb.shape[0] < encoder_hidden_states.shape[0]: # duplicate mode emb for each batch
+                controlnet_mode_emb = controlnet_mode_emb.expand(encoder_hidden_states.shape[0], 1, encoder_hidden_states.shape[2])
+            encoder_hidden_states = torch.cat([controlnet_mode_emb, encoder_hidden_states], dim=1)
+
+        txt_ids = torch.cat((txt_ids[0:1, :], txt_ids), dim=0)
+        ids = torch.cat((txt_ids, img_ids), dim=0)
+        image_rotary_emb = self.pos_embed(ids)
+
+        block_samples = ()
+        for _, block in enumerate(self.transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+            block_samples = block_samples + (hidden_states,)
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        single_block_samples = ()
+        for _, block in enumerate(self.single_transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+            single_block_samples = single_block_samples + (hidden_states[:, encoder_hidden_states.shape[1] :],)
+
+        # controlnet block
+        controlnet_block_samples = ()
+        for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks, strict=False):
+            block_sample = controlnet_block(block_sample)
+            controlnet_block_samples = controlnet_block_samples + (block_sample,)
+
+        controlnet_single_block_samples = ()
+        for single_block_sample, controlnet_block in zip(single_block_samples, self.controlnet_single_blocks, strict=False):
+            single_block_sample = controlnet_block(single_block_sample)
+            controlnet_single_block_samples = controlnet_single_block_samples + (single_block_sample,)
+
+        # scaling
+        controlnet_block_samples = [sample * conditioning_scale for sample in controlnet_block_samples]
+        controlnet_single_block_samples = [sample * conditioning_scale for sample in controlnet_single_block_samples]
+
+        controlnet_block_samples = None if len(controlnet_block_samples) == 0 else controlnet_block_samples
+        controlnet_single_block_samples = (
+            None if len(controlnet_single_block_samples) == 0 else controlnet_single_block_samples
+        )
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (controlnet_block_samples, controlnet_single_block_samples)
+
+        return BriaControlNetOutput(
+            controlnet_block_samples=controlnet_block_samples,
+            controlnet_single_block_samples=controlnet_single_block_samples,
+        )
+
+
+class BriaMultiControlNetModel(ModelMixin):
+    r"""
+    `BriaMultiControlNetModel` wrapper class for Multi-BriaControlNetModel
+    This module is a wrapper for multiple instances of the `BriaControlNetModel`. The `forward()` API is designed to be
+    compatible with `BriaControlNetModel`.
+    Args:
+        controlnets (`List[BriaControlNetModel]`):
+            Provides additional conditioning to the unet during the denoising process. You must set multiple
+            `BriaControlNetModel` as a list.
+    """
+
+    def __init__(self, controlnets):
+        super().__init__()
+        self.nets = nn.ModuleList(controlnets)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        controlnet_cond: List[torch.tensor],
+        controlnet_mode: List[torch.tensor],
+        conditioning_scale: List[float],
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ) -> Union[BriaControlNetOutput, Tuple]:
+        # ControlNet-Union with multiple conditions
+        # only load one ControlNet for saving memories
+        if len(self.nets) == 1 and self.nets[0].union:
+            controlnet = self.nets[0]
+
+            for i, (image, mode, scale) in enumerate(zip(controlnet_cond, controlnet_mode, conditioning_scale, strict=False)):
+                block_samples, single_block_samples = controlnet(
+                    hidden_states=hidden_states,
+                    controlnet_cond=image,
+                    controlnet_mode=mode[:, None],
+                    conditioning_scale=scale,
+                    timestep=timestep,
+                    guidance=guidance,
+                    pooled_projections=pooled_projections,
+                    encoder_hidden_states=encoder_hidden_states,
+                    txt_ids=txt_ids,
+                    img_ids=img_ids,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                    return_dict=return_dict,
+                )
+
+                # merge samples
+                if i == 0:
+                    control_block_samples = block_samples
+                    control_single_block_samples = single_block_samples
+                else:
+                    control_block_samples = [
+                        control_block_sample + block_sample
+                        for control_block_sample, block_sample in zip(control_block_samples, block_samples, strict=False)
+                    ]
+
+                    control_single_block_samples = [
+                        control_single_block_sample + block_sample
+                        for control_single_block_sample, block_sample in zip(
+                            control_single_block_samples, single_block_samples, strict=False
+                        )
+                    ]
+
+        # Regular Multi-ControlNets
+        # load all ControlNets into memories
+        else:
+            for i, (image, mode, scale, controlnet) in enumerate(
+                zip(controlnet_cond, controlnet_mode, conditioning_scale, self.nets, strict=False)
+            ):
+                block_samples, single_block_samples = controlnet(
+                    hidden_states=hidden_states,
+                    controlnet_cond=image,
+                    controlnet_mode=mode[:, None],
+                    conditioning_scale=scale,
+                    timestep=timestep,
+                    guidance=guidance,
+                    pooled_projections=pooled_projections,
+                    encoder_hidden_states=encoder_hidden_states,
+                    txt_ids=txt_ids,
+                    img_ids=img_ids,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                    return_dict=return_dict,
+                )
+
+                # merge samples
+                if i == 0:
+                    control_block_samples = block_samples
+                    control_single_block_samples = single_block_samples
+                else:
+                    if block_samples is not None and control_block_samples is not None:
+                        control_block_samples = [
+                            control_block_sample + block_sample
+                            for control_block_sample, block_sample in zip(control_block_samples, block_samples, strict=False)
+                        ]
+                    if single_block_samples is not None and control_single_block_samples is not None:
+                        control_single_block_samples = [
+                            control_single_block_sample + block_sample
+                            for control_single_block_sample, block_sample in zip(
+                                control_single_block_samples, single_block_samples, strict=False
+                            )
+                        ]
+
+        return control_block_samples, control_single_block_samples

invokeai/backend/bria/controlnet_utils.py

@@ -0,0 +1,67 @@
+from typing import List, Tuple
+
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from PIL import Image
+
+
+@torch.no_grad()
+def prepare_control_images(
+    vae: AutoencoderKL,
+    control_images: list[Image.Image],
+    control_modes: list[int],
+    width: int,
+    height: int,
+    device: torch.device,
+) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
+
+    tensored_control_images = []
+    tensored_control_modes = []
+    for idx, control_image_ in enumerate(control_images):
+        tensored_control_image = _prepare_image(
+            image=control_image_,
+            width=width,
+            height=height,
+            device=device,
+            dtype=vae.dtype,
+        )
+        height, width = tensored_control_image.shape[-2:]
+
+        # vae encode
+        tensored_control_image = vae.encode(tensored_control_image).latent_dist.sample()
+        tensored_control_image = (tensored_control_image) * vae.config.scaling_factor
+
+        # pack
+        height_control_image, width_control_image = tensored_control_image.shape[2:]
+        tensored_control_image = _pack_latents(
+            tensored_control_image,
+            height_control_image,
+            width_control_image,
+        )
+        tensored_control_images.append(tensored_control_image)
+        tensored_control_modes.append(torch.tensor(control_modes[idx]).expand(
+            tensored_control_image.shape[0]).to(device, dtype=torch.long))
+
+    return tensored_control_images, tensored_control_modes
+
+def _prepare_image(
+    image: Image.Image,
+    width: int,
+    height: int,
+    device: torch.device,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    image = image.convert("RGB")
+    image = VaeImageProcessor(vae_scale_factor=16).preprocess(image, height=height, width=width)
+    image = image.repeat_interleave(1, dim=0)
+    image = image.to(device=device, dtype=dtype)
+    return image
+
+def _pack_latents(latents, height, width):
+    latents = latents.view(1, 4, height // 2, 2, width // 2, 2)
+    latents = latents.permute(0, 2, 4, 1, 3, 5)
+    latents = latents.reshape(1, (height // 2) * (width // 2), 16)
+
+    return latents
+

invokeai/backend/bria/pipeline_bria.py

@@ -0,0 +1,640 @@
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import diffusers
+import numpy as np
+import torch
+from diffusers import AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import FluxLoraLoaderMixin
+from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import (
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+
+from invokeai.backend.bria.bria_utils import get_original_sigmas, get_t5_prompt_embeds, is_ng_none
+from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import StableDiffusion3Pipeline
+
+        >>> pipe = StableDiffusion3Pipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16
+        ... )
+        >>> pipe.to("cuda")
+        >>> prompt = "A cat holding a sign that says hello world"
+        >>> image = pipe(prompt).images[0]
+        >>> image.save("sd3.png")
+        ```
+"""
+
+T5_PRECISION = torch.float16
+
+"""
+Based on FluxPipeline with several changes:
+- no pooled embeddings
+- We use zero padding for prompts
+- No guidance embedding since this is not a distilled version
+"""
+class BriaPipeline(FluxPipeline):
+    r"""
+    Args:
+        transformer ([`SD3Transformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder. Stable Diffusion 3 uses
+            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
+            [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+        tokenizer (`T5TokenizerFast`):
+            Tokenizer of class
+            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+    """
+
+    def __init__(
+        self,
+        transformer: BriaTransformer2DModel,
+        scheduler: Union[FlowMatchEulerDiscreteScheduler,KarrasDiffusionSchedulers],
+        vae: AutoencoderKL,
+        text_encoder: T5EncoderModel,
+        tokenizer: T5TokenizerFast
+    ):
+        self.register_modules(
+            vae=vae,
+            transformer=transformer,
+            scheduler=scheduler,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+        )
+
+        # TODO - why different than offical flux (-1)
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels)) if hasattr(self, "vae") and self.vae is not None else 16
+        )
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.default_sample_size = 64 # due to patchify=> 128,128 => res of 1k,1k
+
+        # T5 is senstive to precision so we use the precision used for precompute and cast as needed
+
+        if self.vae.config.shift_factor is None:
+            self.vae.config.shift_factor=0
+            self.vae.to(dtype=torch.float32)
+
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 128,
+        lora_scale: Optional[float] = None,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+        """
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None and USE_PEFT_BACKEND:
+                scale_lora_layers(self.text_encoder, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = get_t5_prompt_embeds(
+                self.tokenizer,
+                self.text_encoder,
+                prompt=prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+            ).to(dtype=self.transformer.dtype)
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            if not is_ng_none(negative_prompt):
+                negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+                if prompt is not None and type(prompt) is not type(negative_prompt):
+                    raise TypeError(
+                        f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                        f" {type(prompt)}."
+                    )
+                elif batch_size != len(negative_prompt):
+                    raise ValueError(
+                        f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                        f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                        " the batch size of `prompt`."
+                    )
+
+                negative_prompt_embeds = get_t5_prompt_embeds(
+                    self.tokenizer,
+                    self.text_encoder,
+                    prompt=negative_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    max_sequence_length=max_sequence_length,
+                    device=device,
+                ).to(dtype=self.transformer.dtype)
+            else:
+                negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+
+        if self.text_encoder is not None:
+            if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
+        text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
+        text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
+
+        return prompt_embeds, negative_prompt_embeds, text_ids
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+
+    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+    # corresponds to doing no classifier free guidance.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1
+
+    @property
+    def joint_attention_kwargs(self):
+        return self._joint_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 30,
+        timesteps: List[int] = None,
+        guidance_scale: float = 5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
+        max_sequence_length: int = 128,
+        clip_value:Union[None,float] = None,
+        normalize:bool = False
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
+
+        Examples:
+
+          Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        callback_on_step_end_tensor_inputs = ["latents"] if callback_on_step_end_tensor_inputs is None else callback_on_step_end_tensor_inputs
+        self.check_inputs(
+            prompt=prompt,
+            height=height,
+            width=width,
+            prompt_embeds=prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            text_ids
+        ) = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4 # due to patch=2, we devide by 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        if  isinstance(self.scheduler,FlowMatchEulerDiscreteScheduler) and self.scheduler.config['use_dynamic_shifting']:
+            sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+            image_seq_len = latents.shape[1] # Shift by height - Why just height?
+            print(f"Using dynamic shift in pipeline with sequence length {image_seq_len}")
+
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.base_image_seq_len,
+                self.scheduler.config.max_image_seq_len,
+                self.scheduler.config.base_shift,
+                self.scheduler.config.max_shift,
+            )
+            timesteps, num_inference_steps = retrieve_timesteps(
+                self.scheduler,
+                num_inference_steps,
+                device,
+                timesteps,
+                sigmas,
+                mu=mu,
+            )
+        else:
+            # 4. Prepare timesteps
+            # Sample from training sigmas
+            if isinstance(self.scheduler,DDIMScheduler) or isinstance(self.scheduler,EulerAncestralDiscreteScheduler):
+                timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, None, None)
+            else:
+                sigmas = get_original_sigmas(num_train_timesteps=self.scheduler.config.num_train_timesteps,num_inference_steps=num_inference_steps)
+                timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps,sigmas=sigmas)
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # Supprot different diffusers versions
+        if diffusers.__version__>='0.32.0':
+            latent_image_ids=latent_image_ids[0]
+            text_ids=text_ids[0]
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                if not isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                # This is predicts "v" from flow-matching or eps from diffusion
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    cfg_noise_pred_text = noise_pred_text.std()
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if normalize:
+                    noise_pred = noise_pred * (0.7 *(cfg_noise_pred_text/noise_pred.std())) + 0.3 * noise_pred
+
+                if clip_value:
+                    assert clip_value>0
+                    noise_pred = noise_pred.clip(-clip_value,clip_value)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents.to(dtype=torch.float32) / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents.to(dtype=self.vae.dtype), return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        max_sequence_length=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+
+        if max_sequence_length is not None and max_sequence_length > 512:
+            raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
+
+    def to(self, *args, **kwargs):
+        DiffusionPipeline.to(self, *args, **kwargs)
+        # T5 is senstive to precision so we use the precision used for precompute and cast as needed
+        self.text_encoder = self.text_encoder.to(dtype=T5_PRECISION)
+        for block in self.text_encoder.encoder.block:
+            block.layer[-1].DenseReluDense.wo.to(dtype=torch.float32)
+
+        if self.vae.config.shift_factor == 0 and self.vae.dtype!=torch.float32:
+            self.vae.to(dtype=torch.float32)
+
+
+        return self
+
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // self.vae_scale_factor)
+        width = 2 * (int(width) // self.vae_scale_factor )
+
+        shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is not None:
+            latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+            return latents.to(device=device, dtype=dtype), latent_image_ids
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+
+        latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+
+        return latents, latent_image_ids
+
+    @staticmethod
+    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+        latents = latents.permute(0, 2, 4, 1, 3, 5)
+        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+        return latents
+
+    @staticmethod
+    def _unpack_latents(latents, height, width, vae_scale_factor):
+        batch_size, num_patches, channels = latents.shape
+
+        height = height // vae_scale_factor
+        width = width // vae_scale_factor
+
+        latents = latents.view(batch_size, height, width, channels // 4, 2, 2)
+        latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+        latents = latents.reshape(batch_size, channels // (2 * 2), height * 2, width * 2)
+
+        return latents
+
+    @staticmethod
+    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
+        latent_image_ids = torch.zeros(height, width, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids.repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+
+        return latent_image_ids.to(device=device, dtype=dtype)
+
+
+
+
+
+
+

invokeai/backend/bria/pipeline_bria_controlnet.py

@@ -0,0 +1,666 @@
+# Copyright 2024 Stability AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import diffusers
+import numpy as np
+import torch
+from diffusers import AutoencoderKL  # Waiting for diffusers udpdate
+from diffusers.image_processor import PipelineImageInput
+from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
+from diffusers.utils import USE_PEFT_BACKEND, logging
+from diffusers.utils.peft_utils import scale_lora_layers, unscale_lora_layers
+from diffusers.utils.torch_utils import randn_tensor
+from transformers import (
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+
+from invokeai.backend.bria.bria_utils import get_original_sigmas, get_t5_prompt_embeds, is_ng_none
+from invokeai.backend.bria.controlnet_bria import BriaControlNetModel
+from invokeai.backend.bria.pipeline_bria import BriaPipeline
+from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class BriaControlNetPipeline(BriaPipeline):
+    r"""
+    Args:
+        transformer ([`SD3Transformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`T5EncoderModel`]):
+            Frozen text-encoder. Stable Diffusion 3 uses
+            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
+            [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
+        tokenizer (`T5TokenizerFast`):
+            Tokenizer of class
+            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder->transformer->vae"
+    _optional_components = []
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
+
+    def __init__(  # EYAL - removed clip text encoder + tokenizer
+        self,
+        transformer: BriaTransformer2DModel,
+        scheduler: Union[FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers],
+        vae: AutoencoderKL,
+        text_encoder: T5EncoderModel,
+        tokenizer: T5TokenizerFast,
+        controlnet: BriaControlNetModel,
+    ):
+        super().__init__(
+            transformer=transformer, scheduler=scheduler, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer
+        )
+        self.register_modules(controlnet=controlnet)
+
+    def prepare_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        guess_mode=False,
+    ):
+        if isinstance(image, torch.Tensor):
+            pass
+        else:
+            image = self.image_processor.preprocess(image, height=height, width=width)
+
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance and not guess_mode:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    def prepare_control(self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode):
+        num_channels_latents = self.transformer.config.in_channels // 4
+        control_image = self.prepare_image(
+            image=control_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=self.vae.dtype,
+        )
+        height, width = control_image.shape[-2:]
+
+        # vae encode
+        control_image = self.vae.encode(control_image).latent_dist.sample()
+        control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+        # pack
+        height_control_image, width_control_image = control_image.shape[2:]
+        control_image = self._pack_latents(
+            control_image,
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height_control_image,
+            width_control_image,
+        )
+
+        # Here we ensure that `control_mode` has the same length as the control_image.
+        if control_mode is not None:
+            if not isinstance(control_mode, int):
+                raise ValueError(" For `BriaControlNet`, `control_mode` should be an `int` or `None`")
+            control_mode = torch.tensor(control_mode).to(device, dtype=torch.long)
+            control_mode = control_mode.view(-1, 1).expand(control_image.shape[0], 1)
+
+        return control_image, control_mode
+
+    def prepare_multi_control(self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode):
+        num_channels_latents = self.transformer.config.in_channels // 4
+        control_images = []
+        for _, control_image_ in enumerate(control_image):
+            control_image_ = self.prepare_image(
+                image=control_image_,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=self.vae.dtype,
+            )
+            height, width = control_image_.shape[-2:]
+
+            # vae encode
+            control_image_ = self.vae.encode(control_image_).latent_dist.sample()
+            control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+
+            # pack
+            height_control_image, width_control_image = control_image_.shape[2:]
+            control_image_ = self._pack_latents(
+                control_image_,
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height_control_image,
+                width_control_image,
+            )
+            control_images.append(control_image_)
+
+        control_image = control_images
+
+        # Here we ensure that `control_mode` has the same length as the control_image.
+        if isinstance(control_mode, list) and len(control_mode) != len(control_image):
+            raise ValueError(
+                "For Multi-ControlNet, `control_mode` must be a list of the same "
+                + " length as the number of controlnets (control images) specified"
+            )
+        if not isinstance(control_mode, list):
+            control_mode = [control_mode] * len(control_image)
+        # set control mode
+        control_modes = []
+        for cmode in control_mode:
+            if cmode is None:
+                cmode = -1
+            control_mode = torch.tensor(cmode).expand(control_images[0].shape[0]).to(device, dtype=torch.long)
+            control_modes.append(control_mode)
+        control_mode = control_modes
+
+        return control_image, control_mode
+
+    def get_controlnet_keep(self, timesteps, control_guidance_start, control_guidance_end):
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            keeps = [
+                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                for s, e in zip(control_guidance_start, control_guidance_end, strict=False)
+            ]
+            controlnet_keep.append(keeps[0] if isinstance(self.controlnet, BriaControlNetModel) else keeps)
+        return controlnet_keep
+
+    def get_control_start_end(self, control_guidance_start, control_guidance_end):
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+            mult = 1  # TODO - why is this 1?
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
+
+        return control_guidance_start, control_guidance_end
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 30,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
+        control_image: Optional[PipelineImageInput] = None,
+        control_mode: Optional[Union[int, List[int]]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        latent_image_ids: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        text_ids: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
+        max_sequence_length: int = 128,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+                of a plain tuple.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
+        Examples:
+        Returns:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+        control_guidance_start, control_guidance_end = self.get_control_start_end(
+            control_guidance_start=control_guidance_start, control_guidance_end=control_guidance_end
+        )
+
+        # 1. Check inputs. Raise error if not correct
+        callback_on_step_end_tensor_inputs = ["latents"] if callback_on_step_end_tensor_inputs is None else callback_on_step_end_tensor_inputs
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        device = self._execution_device
+
+
+        # 4. Prepare timesteps
+        if  isinstance(self.scheduler,FlowMatchEulerDiscreteScheduler) and self.scheduler.config['use_dynamic_shifting']:
+            sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+
+
+            # Determine image sequence length
+            if control_image is not None:
+                if isinstance(control_image, list):
+                    image_seq_len = control_image[0].shape[1]
+                else:
+                    image_seq_len = control_image.shape[1]
+            else:
+                # Use latents sequence length when no control image is provided
+                image_seq_len = latents.shape[1]
+
+            print(f"Using dynamic shift in pipeline with sequence length {image_seq_len}")
+
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.base_image_seq_len,
+                self.scheduler.config.max_image_seq_len,
+                self.scheduler.config.base_shift,
+                self.scheduler.config.max_shift,
+            )
+            timesteps, num_inference_steps = retrieve_timesteps(
+                self.scheduler,
+                num_inference_steps,
+                device,
+                timesteps=None,
+                sigmas=sigmas,
+                mu=mu,
+            )
+        else:
+            # 5. Prepare timesteps
+            sigmas = get_original_sigmas(
+                num_train_timesteps=self.scheduler.config.num_train_timesteps, num_inference_steps=num_inference_steps
+            )
+            timesteps, num_inference_steps = retrieve_timesteps(
+                self.scheduler, num_inference_steps, device, timesteps, sigmas=sigmas
+            )
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 6. Create tensor stating which controlnets to keep
+        if control_image is not None:
+            controlnet_keep = self.get_controlnet_keep(
+                timesteps=timesteps,
+                control_guidance_start=control_guidance_start,
+                control_guidance_end=control_guidance_end,
+            )
+
+        if diffusers.__version__>='0.32.0':
+            latent_image_ids=latent_image_ids[0]
+            text_ids=text_ids[0]
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+
+        # EYAL - added the CFG loop
+        # 7. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                # if type(self.scheduler) != FlowMatchEulerDiscreteScheduler:
+                if not isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latent_model_input.shape[0])
+
+                # Handling ControlNet
+                if control_image is not None:
+                    if isinstance(controlnet_keep[i], list):
+                        if isinstance(controlnet_conditioning_scale, list):
+                            cond_scale = controlnet_conditioning_scale
+                        else:
+                            cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i], strict=False)]
+                    else:
+                        controlnet_cond_scale = controlnet_conditioning_scale
+                        if isinstance(controlnet_cond_scale, list):
+                            controlnet_cond_scale = controlnet_cond_scale[0]
+                        cond_scale = controlnet_cond_scale * controlnet_keep[i]
+
+                    controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
+                        hidden_states=latents,
+                        controlnet_cond=control_image,
+                        controlnet_mode=control_mode,
+                        conditioning_scale=cond_scale,
+                        timestep=timestep,
+                        # guidance=guidance,
+                        # pooled_projections=pooled_prompt_embeds,
+                        encoder_hidden_states=prompt_embeds,
+                        txt_ids=text_ids,
+                        img_ids=latent_image_ids,
+                        joint_attention_kwargs=self.joint_attention_kwargs,
+                        return_dict=False,
+                    )
+                else:
+                    controlnet_block_samples, controlnet_single_block_samples = None, None
+
+                # This is predicts "v" from flow-matching
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    controlnet_block_samples=controlnet_block_samples,
+                    controlnet_single_block_samples=controlnet_single_block_samples,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents.to(dtype=self.vae.dtype), return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)
+
+
+def encode_prompt(
+        prompt: Union[str, List[str]],
+        tokenizer: T5TokenizerFast,
+        text_encoder: T5EncoderModel,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 128,
+        lora_scale: Optional[float] = None,
+    ):
+    r"""
+
+    Args:
+        prompt (`str` or `List[str]`, *optional*):
+            prompt to be encoded
+        device: (`torch.device`):
+            torch device
+        num_images_per_prompt (`int`):
+            number of images that should be generated per prompt
+        do_classifier_free_guidance (`bool`):
+            whether to use classifier free guidance or not
+        negative_prompt (`str` or `List[str]`, *optional*):
+            The prompt or prompts not to guide the image generation. If not defined, one has to pass
+            `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+            less than `1`).
+        prompt_embeds (`torch.FloatTensor`, *optional*):
+            Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+            provided, text embeddings will be generated from `prompt` input argument.
+        negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+            Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+            weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+            argument.
+    """
+    device = device or torch.device("cuda")
+
+    # set lora scale so that monkey patched LoRA
+    # function of text encoder can correctly access it
+    # dynamically adjust the LoRA scale
+    if text_encoder is not None and USE_PEFT_BACKEND:
+        scale_lora_layers(text_encoder, lora_scale)
+
+    prompt = [prompt] if isinstance(prompt, str) else prompt
+    if prompt is not None:
+        batch_size = len(prompt)
+    else:
+        batch_size = prompt_embeds.shape[0]
+
+    dtype = text_encoder.dtype if text_encoder is not None else torch.float32
+    if prompt_embeds is None:
+        prompt_embeds = get_t5_prompt_embeds(
+            tokenizer,
+            text_encoder,
+            prompt=prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        ).to(dtype=dtype)
+
+    if negative_prompt_embeds is None:
+        if not is_ng_none(negative_prompt):
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds = get_t5_prompt_embeds(
+                tokenizer,
+                text_encoder,
+                prompt=negative_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+            ).to(dtype=dtype)
+        else:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+
+    if text_encoder is not None:
+        if USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(text_encoder, lora_scale)
+
+    text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
+    text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
+
+    return prompt_embeds, negative_prompt_embeds, text_ids
+
+
+def prepare_latents(
+    batch_size: int,
+    num_channels_latents: int,
+    height: int,
+    width: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    generator: torch.Generator,
+    latents: Optional[torch.FloatTensor] = None,
+):
+    # VAE applies 8x compression on images but we must also account for packing which requires
+    # latent height and width to be divisible by 2.
+    vae_scale_factor = 16
+    height = 2 * (int(height) // vae_scale_factor)
+    width = 2 * (int(width) // vae_scale_factor )
+
+    shape = (batch_size, num_channels_latents, height, width)
+
+    if latents is not None:
+        latent_image_ids = _prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+        return latents.to(device=device, dtype=dtype), latent_image_ids
+
+    if isinstance(generator, list) and len(generator) != batch_size:
+        raise ValueError(
+            f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+            f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+        )
+
+    latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+    latents = _pack_latents(latents, batch_size, num_channels_latents, height, width)
+
+    latent_image_ids = _prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
+
+    return latents, latent_image_ids
+
+
+def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
+    latent_image_ids = torch.zeros(height, width, 3)
+    latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
+    latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
+
+    latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+    latent_image_ids = latent_image_ids.repeat(batch_size, 1, 1, 1)
+    latent_image_ids = latent_image_ids.reshape(
+        batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+    )
+
+    return latent_image_ids.to(device=device, dtype=dtype)
+
+
+
+
+def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+    latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+    latents = latents.permute(0, 2, 4, 1, 3, 5)
+    latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+    return latents

invokeai/backend/bria/transformer_bria.py

@@ -0,0 +1,322 @@
+from typing import Any, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.models.embeddings import TimestepEmbedding, get_timestep_embedding
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import AdaLayerNormContinuous
+from diffusers.models.transformers.transformer_flux import FluxSingleTransformerBlock, FluxTransformerBlock
+from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+
+from invokeai.backend.bria.bria_utils import FluxPosEmbed as EmbedND
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class Timesteps(nn.Module):
+    def __init__(
+        self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1, time_theta=10000
+    ):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+        self.scale = scale
+        self.time_theta = time_theta
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+            scale=self.scale,
+            max_period=self.time_theta,
+        )
+        return t_emb
+
+
+class TimestepProjEmbeddings(nn.Module):
+    def __init__(self, embedding_dim, time_theta):
+        super().__init__()
+
+        self.time_proj = Timesteps(
+            num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, time_theta=time_theta
+        )
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+    def forward(self, timestep, dtype):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=dtype))  # (N, D)
+        return timesteps_emb
+
+
+"""
+Based on FluxPipeline with several changes:
+- no pooled embeddings
+- We use zero padding for prompts
+- No guidance embedding since this is not a distilled version
+"""
+
+
+class BriaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
+    """
+    The Transformer model introduced in Flux.
+
+    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
+
+    Parameters:
+        patch_size (`int`): Patch size to turn the input data into small patches.
+        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
+        num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
+        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
+        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
+        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
+        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
+        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 1,
+        in_channels: int = 64,
+        num_layers: int = 19,
+        num_single_layers: int = 38,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 4096,
+        pooled_projection_dim: int = None,
+        guidance_embeds: bool = False,
+        axes_dims_rope: Optional[List[int]] = None,
+        rope_theta=10000,
+        time_theta=10000,
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
+
+        axes_dims_rope = [16, 56, 56] if axes_dims_rope is None else axes_dims_rope
+        self.pos_embed = EmbedND(theta=rope_theta, axes_dim=axes_dims_rope)
+
+        self.time_embed = TimestepProjEmbeddings(embedding_dim=self.inner_dim, time_theta=time_theta)
+
+        # if pooled_projection_dim:
+        #     self.pooled_text_embed = PixArtAlphaTextProjection(pooled_projection_dim, embedding_dim=self.inner_dim, act_fn="silu")
+
+        if guidance_embeds:
+            self.guidance_embed = TimestepProjEmbeddings(embedding_dim=self.inner_dim)
+
+        self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim)
+        self.x_embedder = torch.nn.Linear(self.config.in_channels, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                FluxTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_layers)
+            ]
+        )
+
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                FluxSingleTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_single_layers)
+            ]
+        )
+
+        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
+        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
+
+        self.gradient_checkpointing = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+        controlnet_block_samples=None,
+        controlnet_single_block_samples=None,
+    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+        """
+        The [`FluxTransformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input `hidden_states`.
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+                from the embeddings of input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+                A list of tensors that if specified are added to the residuals of transformer blocks.
+            joint_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+        hidden_states = self.x_embedder(hidden_states)
+
+        timestep = timestep.to(hidden_states.dtype)
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype)
+        else:
+            guidance = None
+
+        # temb = (
+        #     self.time_text_embed(timestep, pooled_projections)
+        #     if guidance is None
+        #     else self.time_text_embed(timestep, guidance, pooled_projections)
+        # )
+
+        temb = self.time_embed(timestep, dtype=hidden_states.dtype)
+
+        # if pooled_projections:
+        #     temb+=self.pooled_text_embed(pooled_projections)
+
+        if guidance:
+            temb += self.guidance_embed(guidance, dtype=hidden_states.dtype)
+
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if len(txt_ids.shape) == 2:
+            ids = torch.cat((txt_ids, img_ids), dim=0)
+        else:
+            ids = torch.cat((txt_ids, img_ids), dim=1)
+        image_rotary_emb = self.pos_embed(ids)
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
+                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+                    + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+
+        hidden_states = self.norm_out(hidden_states, temb)
+        output = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)

invokeai/backend/model_manager/legacy_probe.py

@@ -125,6 +125,8 @@ class ModelProbe(object):
     }
 
     CLASS2TYPE = {
+        "BriaPipeline": ModelType.Main,
+        "BriaTransformer2DModel": ModelType.ControlNet,
         "FluxPipeline": ModelType.Main,
         "StableDiffusionPipeline": ModelType.Main,
         "StableDiffusionInpaintPipeline": ModelType.Main,
@@ -861,6 +863,8 @@ class PipelineFolderProbe(FolderProbeBase):
                 return BaseModelType.StableDiffusion3
             elif transformer_conf["_class_name"] == "CogView4Transformer2DModel":
                 return BaseModelType.CogView4
+            elif transformer_conf["_class_name"] == "BriaTransformer2DModel":
+                return BaseModelType.Bria
             else:
                 raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
 
@@ -1010,6 +1014,9 @@ class ControlNetFolderProbe(FolderProbeBase):
         if config.get("_class_name", None) == "FluxControlNetModel":
             return BaseModelType.Flux
 
+        if config.get("_class_name", None) == "BriaTransformer2DModel":
+            return BaseModelType.Bria
+
         # no obvious way to distinguish between sd2-base and sd2-768
         dimension = config["cross_attention_dim"]
         if dimension == 768:

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

@@ -0,0 +1,95 @@
+from pathlib import Path
+from typing import Optional
+
+from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
+    CheckpointConfigBase,
+    ControlNetCheckpointConfig,
+    ControlNetDiffusersConfig,
+    DiffusersConfigBase,
+)
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+)
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.Bria, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
+class BriaControlNetDiffusersModel(GenericDiffusersLoader):
+    """Class to load Bria control net models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if isinstance(config, ControlNetCheckpointConfig):
+            raise NotImplementedError("CheckpointConfigBase is not implemented for Bria models.")
+
+        model_path = Path(config.path)
+        load_class = self.get_hf_load_class(model_path)
+        repo_variant = config.repo_variant if isinstance(config, ControlNetDiffusersConfig) else None
+        variant = repo_variant.value if repo_variant else None
+        model_path = model_path
+
+        dtype = self._torch_dtype
+
+        try:
+            result: AnyModel = load_class.from_pretrained(
+                model_path,
+                torch_dtype=dtype,
+                variant=variant,
+                use_safetensors=False,
+            )
+        except OSError as e:
+            if variant and "no file named" in str(
+                e
+            ):  # try without the variant, just in case user's preferences changed
+                result = load_class.from_pretrained(model_path, torch_dtype=dtype)
+            else:
+                raise e
+
+        return result
+
+@ModelLoaderRegistry.register(base=BaseModelType.Bria, type=ModelType.Main, format=ModelFormat.Diffusers)
+class BriaDiffusersModel(GenericDiffusersLoader):
+    """Class to load Bria main models."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if isinstance(config, CheckpointConfigBase):
+            raise NotImplementedError("CheckpointConfigBase is not implemented for Bria models.")
+
+        if submodel_type is None:
+            raise Exception("A submodel type must be provided when loading main pipelines.")
+
+        model_path = Path(config.path)
+        load_class = self.get_hf_load_class(model_path, submodel_type)
+        repo_variant = config.repo_variant if isinstance(config, DiffusersConfigBase) else None
+        variant = repo_variant.value if repo_variant else None
+        model_path = model_path / submodel_type.value
+
+        dtype = self._torch_dtype
+        try:
+            result: AnyModel = load_class.from_pretrained(
+                model_path,
+                torch_dtype=dtype,
+                variant=variant,
+            )
+        except OSError as e:
+            if variant and "no file named" in str(
+                e
+            ):  # try without the variant, just in case user's preferences changed
+                result = load_class.from_pretrained(model_path, torch_dtype=dtype)
+            else:
+                raise e
+
+        return result

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

@@ -80,7 +80,13 @@ class GenericDiffusersLoader(ModelLoader):
             "transformers",
             "invokeai.backend.quantization.fast_quantized_transformers_model",
             "invokeai.backend.quantization.fast_quantized_diffusion_model",
+            "transformer_bria",
         ]:
+            if module == "transformer_bria":
+                module = "invokeai.backend.bria.transformer_bria"
+            elif class_name == "BriaTransformer2DModel":
+                class_name = "BriaControlNetModel"
+                module = "invokeai.backend.bria.controlnet_bria"
             res_type = sys.modules[module]
         else:
             res_type = sys.modules["diffusers"].pipelines

invokeai/backend/model_manager/load/model_util.py

@@ -12,6 +12,9 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
 from transformers import CLIPTokenizer, T5Tokenizer, T5TokenizerFast
 
+from invokeai.backend.bria.controlnet_aux.open_pose.body import Body
+from invokeai.backend.bria.controlnet_aux.open_pose.face import Face
+from invokeai.backend.bria.controlnet_aux.open_pose.hand import Hand
 from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
@@ -62,6 +65,8 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
         else:
             # If neither is available, return 0
             return 0
+    elif isinstance(model, (Body, Hand, Face)):
+        return calc_module_size(model.model)
     elif isinstance(
         model,
         (

invokeai/backend/model_manager/taxonomy.py

@@ -30,6 +30,7 @@ class BaseModelType(str, Enum):
     Imagen4 = "imagen4"
     ChatGPT4o = "chatgpt-4o"
     FluxKontext = "flux-kontext"
+    Bria = "bria"
 
 
 class ModelType(str, Enum):

invokeai/frontend/web/.eslintignore

@@ -1,10 +0,0 @@
-dist/
-static/
-.husky/
-node_modules/
-patches/
-stats.html
-index.html
-.yarn/
-*.scss
-src/services/api/schema.ts

invokeai/frontend/web/.eslintrc.js

@@ -1,88 +0,0 @@
-module.exports = {
-  extends: ['@invoke-ai/eslint-config-react'],
-  plugins: ['path', 'i18next'],
-  rules: {
-    // TODO(psyche): Enable this rule. Requires no default exports in components - many changes.
-    'react-refresh/only-export-components': 'off',
-    // TODO(psyche): Enable this rule. Requires a lot of eslint-disable-next-line comments.
-    '@typescript-eslint/consistent-type-assertions': 'off',
-    // https://github.com/qdanik/eslint-plugin-path
-    'path/no-relative-imports': ['error', { maxDepth: 0 }],
-    // https://github.com/edvardchen/eslint-plugin-i18next/blob/HEAD/docs/rules/no-literal-string.md
-    // TODO: ENABLE THIS RULE BEFORE v6.0.0
-    // 'i18next/no-literal-string': 'error',
-    // https://eslint.org/docs/latest/rules/no-console
-    'no-console': 'warn',
-    // 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',
-    // Restrict setActiveTab calls to only use-navigation-api.tsx
-    'no-restricted-syntax': [
-      'error',
-      {
-        selector: 'CallExpression[callee.name="setActiveTab"]',
-        message:
-          'setActiveTab() can only be called from use-navigation-api.tsx. Use navigationApi.switchToTab() instead.',
-      },
-    ],
-    // TODO: ENABLE THIS RULE BEFORE v6.0.0
-    'react/display-name': 'off',
-    'no-restricted-properties': [
-      'error',
-      {
-        object: 'crypto',
-        property: 'randomUUID',
-        message: 'Use of crypto.randomUUID is not allowed as it is not available in all browsers.',
-      },
-      {
-        object: 'navigator',
-        property: 'clipboard',
-        message:
-          'The Clipboard API is not available by default in Firefox. Use the `useClipboard` hook instead, which wraps clipboard access to prevent errors.',
-      },
-    ],
-    'no-restricted-imports': [
-      'error',
-      {
-        paths: [
-          {
-            name: 'lodash-es',
-            importNames: ['isEqual'],
-            message: 'Please use objectEquals from @observ33r/object-equals instead.',
-          },
-          {
-            name: 'lodash-es',
-            message: 'Please use es-toolkit instead.',
-          },
-          {
-            name: 'es-toolkit',
-            importNames: ['isEqual'],
-            message: 'Please use objectEquals from @observ33r/object-equals instead.',
-          },
-        ],
-      },
-    ],
-  },
-  overrides: [
-    /**
-     * Allow setActiveTab calls only in use-navigation-api.tsx
-     */
-    {
-      files: ['**/use-navigation-api.tsx'],
-      rules: {
-        'no-restricted-syntax': 'off',
-      },
-    },
-    /**
-     * Overrides for stories
-     */
-    {
-      files: ['*.stories.tsx'],
-      rules: {
-        // We may not have i18n available in stories.
-        'i18next/no-literal-string': 'off',
-      },
-    },
-  ],
-};

invokeai/frontend/web/.prettierignore

@@ -14,3 +14,4 @@ static/
 src/theme/css/overlayscrollbars.css
 src/theme_/css/overlayscrollbars.css
 pnpm-lock.yaml
+.claude

invokeai/frontend/web/.prettierrc.js

@@ -1,11 +0,0 @@
-module.exports = {
-  ...require('@invoke-ai/prettier-config-react'),
-  overrides: [
-    {
-      files: ['public/locales/*.json'],
-      options: {
-        tabWidth: 4,
-      },
-    },
-  ],
-};

invokeai/frontend/web/.prettierrc.json

@@ -0,0 +1,17 @@
+{
+  "$schema": "http://json.schemastore.org/prettierrc",
+  "trailingComma": "es5",
+  "printWidth": 120,
+  "tabWidth": 2,
+  "semi": true,
+  "singleQuote": true,
+  "endOfLine": "auto",
+  "overrides": [
+    {
+      "files": ["public/locales/*.json"],
+      "options": {
+        "tabWidth": 4
+      }
+    }
+  ]
+}

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

@@ -1,21 +1,23 @@
-import { PropsWithChildren, memo, useEffect } from 'react';
-import { modelChanged } from '../src/features/controlLayers/store/paramsSlice';
-import { useAppDispatch } from '../src/app/store/storeHooks';
 import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
+import type { PropsWithChildren } from 'react';
+import { memo, useEffect } from 'react';
+
+import { useAppDispatch } from '../src/app/store/storeHooks';
+import { modelChanged } from '../src/features/controlLayers/store/paramsSlice';
 /**
  * Initializes some state for storybook. Must be in a different component
  * so that it is run inside the redux context.
  */
-export const ReduxInit = memo((props: PropsWithChildren) => {
+export const ReduxInit = memo(({ children }: PropsWithChildren) => {
   const dispatch = useAppDispatch();
   useGlobalModifiersInit();
   useEffect(() => {
     dispatch(
       modelChanged({ model: { key: 'test_model', hash: 'some_hash', name: 'some name', base: 'sd-1', type: 'main' } })
     );
-  }, []);
+  }, [dispatch]);
 
-  return props.children;
+  return children;
 });
 
 ReduxInit.displayName = 'ReduxInit';

invokeai/frontend/web/.storybook/main.ts

@@ -2,19 +2,13 @@ import type { StorybookConfig } from '@storybook/react-vite';
 
 const config: StorybookConfig = {
   stories: ['../src/**/*.mdx', '../src/**/*.stories.@(js|jsx|mjs|ts|tsx)'],
-  addons: [
-    '@storybook/addon-links',
-    '@storybook/addon-essentials',
-    '@storybook/addon-interactions',
-    '@storybook/addon-storysource',
-  ],
+  addons: ['@storybook/addon-links', '@storybook/addon-docs'],
+
   framework: {
     name: '@storybook/react-vite',
     options: {},
   },
-  docs: {
-    autodocs: 'tag',
-  },
+
   core: {
     disableTelemetry: true,
   },

invokeai/frontend/web/.storybook/manager.ts

@@ -1,5 +1,5 @@
-import { addons } from '@storybook/manager-api';
-import { themes } from '@storybook/theming';
+import { addons } from 'storybook/manager-api';
+import { themes } from 'storybook/theming';
 
 addons.setConfig({
   theme: themes.dark,

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

@@ -1,17 +1,18 @@
-import { Preview } from '@storybook/react';
-import { themes } from '@storybook/theming';
+import type { Preview } from '@storybook/react-vite';
+import { themes } from 'storybook/theming';
+import { $store } from 'app/store/nanostores/store';
 import i18n from 'i18next';
 import { initReactI18next } from 'react-i18next';
 import { Provider } from 'react-redux';
-import ThemeLocaleProvider from '../src/app/components/ThemeLocaleProvider';
-import { $baseUrl } from '../src/app/store/nanostores/baseUrl';
-import { createStore } from '../src/app/store/store';
+
 // TODO: Disabled for IDE performance issues with our translation JSON
 // eslint-disable-next-line @typescript-eslint/ban-ts-comment
 // @ts-ignore
 import translationEN from '../public/locales/en.json';
+import ThemeLocaleProvider from '../src/app/components/ThemeLocaleProvider';
+import { $baseUrl } from '../src/app/store/nanostores/baseUrl';
+import { createStore } from '../src/app/store/store';
 import { ReduxInit } from './ReduxInit';
-import { $store } from 'app/store/nanostores/store';
 
 i18n.use(initReactI18next).init({
   lng: 'en',
@@ -46,6 +47,7 @@ const preview: Preview = {
   parameters: {
     docs: {
       theme: themes.dark,
+      codePanel: true,
     },
   },
 };

invokeai/frontend/web/eslint.config.mjs

@@ -0,0 +1,242 @@
+import js from '@eslint/js';
+import typescriptEslint from '@typescript-eslint/eslint-plugin';
+import typescriptParser from '@typescript-eslint/parser';
+import pluginI18Next from 'eslint-plugin-i18next';
+import pluginImport from 'eslint-plugin-import';
+import pluginPath from 'eslint-plugin-path';
+import pluginReact from 'eslint-plugin-react';
+import pluginReactHooks from 'eslint-plugin-react-hooks';
+import pluginReactRefresh from 'eslint-plugin-react-refresh';
+import pluginSimpleImportSort from 'eslint-plugin-simple-import-sort';
+import pluginStorybook from 'eslint-plugin-storybook';
+import pluginUnusedImports from 'eslint-plugin-unused-imports';
+import globals from 'globals';
+
+export default [
+  js.configs.recommended,
+
+  {
+    languageOptions: {
+      parser: typescriptParser,
+      parserOptions: {
+        ecmaFeatures: {
+          jsx: true,
+        },
+      },
+      globals: {
+        ...globals.browser,
+        ...globals.node,
+        GlobalCompositeOperation: 'readonly',
+        RequestInit: 'readonly',
+      },
+    },
+
+    files: ['**/*.ts', '**/*.tsx', '**/*.js', '**/*.jsx'],
+
+    plugins: {
+      react: pluginReact,
+      '@typescript-eslint': typescriptEslint,
+      'react-hooks': pluginReactHooks,
+      import: pluginImport,
+      'unused-imports': pluginUnusedImports,
+      'simple-import-sort': pluginSimpleImportSort,
+      'react-refresh': pluginReactRefresh.configs.vite,
+      path: pluginPath,
+      i18next: pluginI18Next,
+      storybook: pluginStorybook,
+    },
+
+    rules: {
+      ...typescriptEslint.configs.recommended.rules,
+      ...pluginReact.configs.recommended.rules,
+      ...pluginReact.configs['jsx-runtime'].rules,
+      ...pluginReactHooks.configs.recommended.rules,
+      ...pluginStorybook.configs.recommended.rules,
+
+      'react/jsx-no-bind': [
+        'error',
+        {
+          allowBind: true,
+        },
+      ],
+
+      'react/jsx-curly-brace-presence': [
+        'error',
+        {
+          props: 'never',
+          children: 'never',
+        },
+      ],
+
+      'react-hooks/exhaustive-deps': 'error',
+
+      curly: 'error',
+      'no-var': 'error',
+      'brace-style': 'error',
+      'prefer-template': 'error',
+      radix: 'error',
+      'space-before-blocks': 'error',
+      eqeqeq: 'error',
+      'one-var': ['error', 'never'],
+      'no-eval': 'error',
+      'no-extend-native': 'error',
+      'no-implied-eval': 'error',
+      'no-label-var': 'error',
+      'no-return-assign': 'error',
+      'no-sequences': 'error',
+      'no-template-curly-in-string': 'error',
+      'no-throw-literal': 'error',
+      'no-unmodified-loop-condition': 'error',
+      'import/no-duplicates': 'error',
+      'import/prefer-default-export': 'off',
+      'unused-imports/no-unused-imports': 'error',
+
+      'unused-imports/no-unused-vars': [
+        'error',
+        {
+          vars: 'all',
+          varsIgnorePattern: '^_',
+          args: 'after-used',
+          argsIgnorePattern: '^_',
+        },
+      ],
+
+      'simple-import-sort/imports': 'error',
+      'simple-import-sort/exports': 'error',
+      '@typescript-eslint/no-unused-vars': 'off',
+
+      '@typescript-eslint/ban-ts-comment': [
+        'error',
+        {
+          'ts-expect-error': 'allow-with-description',
+          'ts-ignore': true,
+          'ts-nocheck': true,
+          'ts-check': false,
+          minimumDescriptionLength: 10,
+        },
+      ],
+
+      '@typescript-eslint/no-empty-interface': [
+        'error',
+        {
+          allowSingleExtends: true,
+        },
+      ],
+
+      '@typescript-eslint/consistent-type-imports': [
+        'error',
+        {
+          prefer: 'type-imports',
+          fixStyle: 'separate-type-imports',
+          disallowTypeAnnotations: true,
+        },
+      ],
+
+      '@typescript-eslint/no-import-type-side-effects': 'error',
+
+      '@typescript-eslint/consistent-type-assertions': [
+        'error',
+        {
+          assertionStyle: 'as',
+        },
+      ],
+
+      'path/no-relative-imports': [
+        'error',
+        {
+          maxDepth: 0,
+        },
+      ],
+
+      'no-console': 'warn',
+      'no-promise-executor-return': 'error',
+      'require-await': 'error',
+
+      'no-restricted-syntax': [
+        'error',
+        {
+          selector: 'CallExpression[callee.name="setActiveTab"]',
+          message:
+            'setActiveTab() can only be called from use-navigation-api.tsx. Use navigationApi.switchToTab() instead.',
+        },
+      ],
+
+      'no-restricted-properties': [
+        'error',
+        {
+          object: 'crypto',
+          property: 'randomUUID',
+          message: 'Use of crypto.randomUUID is not allowed as it is not available in all browsers.',
+        },
+        {
+          object: 'navigator',
+          property: 'clipboard',
+          message:
+            'The Clipboard API is not available by default in Firefox. Use the `useClipboard` hook instead, which wraps clipboard access to prevent errors.',
+        },
+      ],
+
+      // Typescript handles this for us: https://eslint.org/docs/latest/rules/no-redeclare#handled_by_typescript
+      'no-redeclare': 'off',
+
+      'no-restricted-imports': [
+        'error',
+        {
+          paths: [
+            {
+              name: 'lodash-es',
+              importNames: ['isEqual'],
+              message: 'Please use objectEquals from @observ33r/object-equals instead.',
+            },
+            {
+              name: 'lodash-es',
+              message: 'Please use es-toolkit instead.',
+            },
+            {
+              name: 'es-toolkit',
+              importNames: ['isEqual'],
+              message: 'Please use objectEquals from @observ33r/object-equals instead.',
+            },
+          ],
+        },
+      ],
+    },
+
+    settings: {
+      react: {
+        version: 'detect',
+      },
+    },
+  },
+
+  {
+    files: ['**/use-navigation-api.tsx'],
+    rules: {
+      'no-restricted-syntax': 'off',
+    },
+  },
+
+  {
+    files: ['**/*.stories.tsx'],
+    rules: {
+      'i18next/no-literal-string': 'off',
+    },
+  },
+
+  {
+    ignores: [
+      '**/dist/',
+      '**/static/',
+      '**/.husky/',
+      '**/node_modules/',
+      '**/patches/',
+      '**/stats.html',
+      '**/index.html',
+      '**/.yarn/',
+      '**/*.scss',
+      'src/services/api/schema.ts',
+      '.prettierrc.js',
+      '.storybook',
+    ],
+  },
+];

invokeai/frontend/web/knip.ts

@@ -14,6 +14,7 @@ const config: KnipConfig = {
     'src/features/controlLayers/konva/util.ts',
     // Will be using this
     'src/common/hooks/useAsyncState.ts',
+    'src/app/store/use-debounced-app-selector.ts',
   ],
   ignoreBinaries: ['only-allow'],
   paths: {

invokeai/frontend/web/package.json

@@ -47,25 +47,25 @@
     "@fontsource-variable/inter": "^5.2.6",
     "@invoke-ai/ui-library": "^0.0.46",
     "@nanostores/react": "^1.0.0",
-    "@observ33r/object-equals": "^1.1.4",
+    "@observ33r/object-equals": "^1.1.5",
     "@reduxjs/toolkit": "2.8.2",
     "@roarr/browser-log-writer": "^1.3.0",
-    "@xyflow/react": "^12.7.1",
-    "ag-psd": "^28.2.1",
+    "@xyflow/react": "^12.8.2",
+    "ag-psd": "^28.2.2",
     "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.2",
     "cmdk": "^1.1.1",
     "compare-versions": "^6.1.1",
-    "dockview": "^4.4.0",
-    "es-toolkit": "^1.39.5",
+    "dockview": "^4.4.1",
+    "es-toolkit": "^1.39.7",
     "filesize": "^10.1.6",
     "fracturedjsonjs": "^4.1.0",
     "framer-motion": "^11.10.0",
-    "i18next": "^25.2.1",
+    "i18next": "^25.3.2",
     "i18next-http-backend": "^3.0.2",
-    "idb-keyval": "6.2.1",
+    "idb-keyval": "6.2.2",
     "jsondiffpatch": "^0.7.3",
-    "konva": "^9.3.20",
+    "konva": "^9.3.22",
     "linkify-react": "^4.3.1",
     "linkifyjs": "^4.3.1",
     "lru-cache": "^11.1.0",
@@ -83,7 +83,7 @@
     "react-dom": "^18.3.1",
     "react-dropzone": "^14.3.8",
     "react-error-boundary": "^5.0.0",
-    "react-hook-form": "^7.58.1",
+    "react-hook-form": "^7.60.0",
     "react-hotkeys-hook": "4.5.0",
     "react-i18next": "^15.5.3",
     "react-icons": "^5.5.0",
@@ -103,7 +103,7 @@
     "use-debounce": "^10.0.5",
     "use-device-pixel-ratio": "^1.1.2",
     "uuid": "^11.1.0",
-    "zod": "^3.25.67",
+    "zod": "^4.0.5",
     "zod-validation-error": "^3.5.2"
   },
   "peerDependencies": {
@@ -111,39 +111,43 @@
     "react-dom": "^18.2.0"
   },
   "devDependencies": {
-    "@invoke-ai/eslint-config-react": "^0.0.14",
-    "@invoke-ai/prettier-config-react": "^0.0.7",
-    "@storybook/addon-essentials": "^8.6.12",
-    "@storybook/addon-interactions": "^8.6.12",
-    "@storybook/addon-links": "^8.6.12",
-    "@storybook/addon-storysource": "^8.6.12",
-    "@storybook/manager-api": "^8.6.12",
-    "@storybook/react": "^8.6.12",
-    "@storybook/react-vite": "^8.6.12",
-    "@storybook/theming": "^8.6.12",
+    "@eslint/js": "^9.31.0",
+    "@storybook/addon-docs": "^9.0.17",
+    "@storybook/addon-links": "^9.0.17",
+    "@storybook/react-vite": "^9.0.17",
     "@types/node": "^22.15.1",
     "@types/react": "^18.3.11",
     "@types/react-dom": "^18.3.0",
     "@types/uuid": "^10.0.0",
+    "@typescript-eslint/eslint-plugin": "^8.37.0",
+    "@typescript-eslint/parser": "^8.37.0",
     "@vitejs/plugin-react-swc": "^3.9.0",
     "@vitest/coverage-v8": "^3.1.2",
     "@vitest/ui": "^3.1.2",
     "concurrently": "^9.1.2",
     "csstype": "^3.1.3",
     "dpdm": "^3.14.0",
-    "eslint": "^8.57.1",
-    "eslint-plugin-i18next": "^6.1.1",
-    "eslint-plugin-path": "^1.3.0",
+    "eslint": "^9.31.0",
+    "eslint-plugin-i18next": "^6.1.2",
+    "eslint-plugin-import": "^2.29.1",
+    "eslint-plugin-path": "^2.0.3",
+    "eslint-plugin-react": "^7.33.2",
+    "eslint-plugin-react-hooks": "^5.2.0",
+    "eslint-plugin-react-refresh": "^0.4.5",
+    "eslint-plugin-simple-import-sort": "^12.0.0",
+    "eslint-plugin-storybook": "^9.0.17",
+    "eslint-plugin-unused-imports": "^4.1.4",
+    "globals": "^16.3.0",
     "knip": "^5.61.3",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^7.6.1",
     "prettier": "^3.5.3",
-    "rollup-plugin-visualizer": "^5.14.0",
-    "storybook": "^8.6.12",
+    "rollup-plugin-visualizer": "^6.0.3",
+    "storybook": "^9.0.17",
     "tsafe": "^1.8.5",
     "type-fest": "^4.40.0",
     "typescript": "^5.8.3",
-    "vite": "^7.0.2",
+    "vite": "^7.0.5",
     "vite-plugin-css-injected-by-js": "^3.5.2",
     "vite-plugin-dts": "^4.5.3",
     "vite-plugin-eslint": "^1.8.1",

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

@@ -574,6 +574,10 @@
                 "title": "Transform",
                 "desc": "Transform the selected layer."
             },
+            "invertMask": {
+                "title": "Invert Mask",
+                "desc": "Invert the selected inpaint mask, creating a new mask with opposite transparency."
+            },
             "applyFilter": {
                 "title": "Apply Filter",
                 "desc": "Apply the pending filter to the selected layer."
@@ -599,6 +603,10 @@
             "toggleNonRasterLayers": {
                 "title": "Toggle Non-Raster Layers",
                 "desc": "Show or hide all non-raster layer categories (Control Layers, Inpaint Masks, Regional Guidance)."
+            },
+            "fitBboxToMasks": {
+                "title": "Fit Bbox To Masks",
+                "desc": "Automatically adjust the generation bounding box to fit visible inpaint masks"
             }
         },
         "workflows": {
@@ -1125,7 +1133,23 @@
         "addItem": "Add Item",
         "generateValues": "Generate Values",
         "floatRangeGenerator": "Float Range Generator",
-        "integerRangeGenerator": "Integer Range Generator"
+        "integerRangeGenerator": "Integer Range Generator",
+        "layout": {
+            "autoLayout": "Auto Layout",
+            "layeringStrategy": "Layering Strategy",
+            "networkSimplex": "Network Simplex",
+            "longestPath": "Longest Path",
+            "nodeSpacing": "Node Spacing",
+            "layerSpacing": "Layer Spacing",
+            "layoutDirection": "Layout Direction",
+            "layoutDirectionRight": "Right",
+            "layoutDirectionDown": "Down",
+            "alignment": "Node Alignment",
+            "alignmentUL": "Top Left",
+            "alignmentDL": "Bottom Left",
+            "alignmentUR": "Top Right",
+            "alignmentDR": "Bottom Right"
+        }
     },
     "parameters": {
         "aspect": "Aspect",
@@ -1407,7 +1431,15 @@
         "sentToUpscale": "Sent to Upscale",
         "promptGenerationStarted": "Prompt generation started",
         "uploadAndPromptGenerationFailed": "Failed to upload image and generate prompt",
-        "promptExpansionFailed": "We ran into an issue. Please try prompt expansion again."
+        "promptExpansionFailed": "We ran into an issue. Please try prompt expansion again.",
+        "maskInverted": "Mask Inverted",
+        "maskInvertFailed": "Failed to Invert Mask",
+        "noVisibleMasks": "No Visible Masks",
+        "noVisibleMasksDesc": "Create or enable at least one inpaint mask to invert",
+        "noInpaintMaskSelected": "No Inpaint Mask Selected",
+        "noInpaintMaskSelectedDesc": "Select an inpaint mask to invert",
+        "invalidBbox": "Invalid Bounding Box",
+        "invalidBboxDesc": "The bounding box has no valid dimensions"
     },
     "popovers": {
         "clipSkip": {
@@ -1775,6 +1807,20 @@
                 "Structure controls how closely the output image will keep to the layout of the original. Low structure allows major changes, while high structure strictly maintains the original composition and layout."
             ]
         },
+        "tileSize": {
+            "heading": "Tile Size",
+            "paragraphs": [
+                "Controls the size of tiles used during the upscaling process. Larger tiles use more memory but may produce better results.",
+                "SD1.5 models default to 768, while SDXL models default to 1024. Reduce tile size if you encounter memory issues."
+            ]
+        },
+        "tileOverlap": {
+            "heading": "Tile Overlap",
+            "paragraphs": [
+                "Controls the overlap between adjacent tiles during upscaling. Higher overlap values help reduce visible seams between tiles but use more memory.",
+                "The default value of 128 works well for most cases, but you can adjust based on your specific needs and memory constraints."
+            ]
+        },
         "fluxDevLicense": {
             "heading": "Non-Commercial License",
             "paragraphs": [
@@ -1926,6 +1972,7 @@
         "canvas": "Canvas",
         "bookmark": "Bookmark for Quick Switch",
         "fitBboxToLayers": "Fit Bbox To Layers",
+        "fitBboxToMasks": "Fit Bbox To Masks",
         "removeBookmark": "Remove Bookmark",
         "saveCanvasToGallery": "Save Canvas to Gallery",
         "saveBboxToGallery": "Save Bbox to Gallery",
@@ -1962,7 +2009,6 @@
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
         "outputOnlyMaskedRegions": "Output Only Generated Regions",
-        "saveAllImagesToGallery": "Save All Images to Gallery",
         "addLayer": "Add Layer",
         "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
@@ -1991,6 +2037,7 @@
         "rasterLayer": "Raster Layer",
         "controlLayer": "Control Layer",
         "inpaintMask": "Inpaint Mask",
+        "invertMask": "Invert Mask",
         "regionalGuidance": "Regional Guidance",
         "referenceImageRegional": "Reference Image (Regional)",
         "referenceImageGlobal": "Reference Image (Global)",
@@ -2087,9 +2134,9 @@
         "resetCanvasLayers": "Reset Canvas Layers",
         "resetGenerationSettings": "Reset Generation Settings",
         "replaceCurrent": "Replace Current",
-        "controlLayerEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, <PullBboxButton>pull the bounding box into this layer</PullBboxButton>, or draw on the canvas to get started.",
-        "referenceImageEmptyStateWithCanvasOptions": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this Reference Image or <PullBboxButton>pull the bounding box into this Reference Image</PullBboxButton> to get started.",
-        "referenceImageEmptyState": "<UploadButton>Upload an image</UploadButton> or drag an image from the <GalleryButton>gallery</GalleryButton> onto this Reference Image to get started.",
+        "controlLayerEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the gallery onto this layer, <PullBboxButton>pull the bounding box into this layer</PullBboxButton>, or draw on the canvas to get started.",
+        "referenceImageEmptyStateWithCanvasOptions": "<UploadButton>Upload an image</UploadButton>, drag an image from the gallery onto this Reference Image or <PullBboxButton>pull the bounding box into this Reference Image</PullBboxButton> to get started.",
+        "referenceImageEmptyState": "<UploadButton>Upload an image</UploadButton> or drag an image from the gallery onto this Reference Image to get started.",
         "uploadOrDragAnImage": "Drag an image from the gallery or <UploadButton>upload an image</UploadButton>.",
         "imageNoise": "Image Noise",
         "denoiseLimit": "Denoise Limit",
@@ -2332,7 +2379,8 @@
                 "alert": "Preserving Masked Region"
             },
             "saveAllImagesToGallery": {
-                "alert": "Saving All Images to Gallery"
+                "label": "Send New Generations to Gallery",
+                "alert": "Sending new generations to Gallery, bypassing Canvas"
             },
             "isolatedStagingPreview": "Isolated Staging Preview",
             "isolatedPreview": "Isolated Preview",
@@ -2396,6 +2444,9 @@
         "upscaleModel": "Upscale Model",
         "postProcessingModel": "Post-Processing Model",
         "scale": "Scale",
+        "tileControl": "Tile Control",
+        "tileSize": "Tile Size",
+        "tileOverlap": "Tile Overlap",
         "postProcessingMissingModelWarning": "Visit the <LinkComponent>Model Manager</LinkComponent> to install a post-processing (image to image) model.",
         "missingModelsWarning": "Visit the <LinkComponent>Model Manager</LinkComponent> to install the required models:",
         "mainModelDesc": "Main model (SD1.5 or SDXL architecture)",

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

@@ -30,16 +30,16 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
   }, [clearStorage]);
 
   return (
-    <ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
-      <ThemeLocaleProvider>
+    <ThemeLocaleProvider>
+      <ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
         <Box id="invoke-app-wrapper" w="100dvw" h="100dvh" position="relative" overflow="hidden">
           <AppContent />
           {!didStudioInit && <Loading />}
         </Box>
         <GlobalHookIsolator config={config} studioInitAction={studioInitAction} />
         <GlobalModalIsolator />
-      </ThemeLocaleProvider>
-    </ErrorBoundary>
+      </ErrorBoundary>
+    </ThemeLocaleProvider>
   );
 };
 

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

@@ -2,6 +2,7 @@ import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
 import { setupListeners } from '@reduxjs/toolkit/query';
 import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
 import { useStudioInitAction } from 'app/hooks/useStudioInitAction';
+import { useSyncLangDirection } from 'app/hooks/useSyncLangDirection';
 import { useSyncQueueStatus } from 'app/hooks/useSyncQueueStatus';
 import { useLogger } from 'app/logging/useLogger';
 import { useSyncLoggingConfig } from 'app/logging/useSyncLoggingConfig';
@@ -15,6 +16,8 @@ import { useDndMonitor } from 'features/dnd/useDndMonitor';
 import { useDynamicPromptsWatcher } from 'features/dynamicPrompts/hooks/useDynamicPromptsWatcher';
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
 import { useWorkflowBuilderWatcher } from 'features/nodes/components/sidePanel/workflow/IsolatedWorkflowBuilderWatcher';
+import { useSyncExecutionState } from 'features/nodes/hooks/useNodeExecutionState';
+import { useSyncNodeErrors } from 'features/nodes/store/util/fieldValidators';
 import { useReadinessWatcher } from 'features/queue/store/readiness';
 import { configChanged } from 'features/system/store/configSlice';
 import { selectLanguage } from 'features/system/store/systemSelectors';
@@ -47,10 +50,13 @@ export const GlobalHookIsolator = memo(
     useCloseChakraTooltipsOnDragFix();
     useNavigationApi();
     useDndMonitor();
+    useSyncNodeErrors();
+    useSyncLangDirection();
 
     // Persistent subscription to the queue counts query - canvas relies on this to know if there are pending
     // and/or in progress canvas sessions.
     useGetQueueCountsByDestinationQuery(queueCountArg);
+    useSyncExecutionState();
 
     useEffect(() => {
       i18n.changeLanguage(language);

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

@@ -1,10 +1,6 @@
 import { GlobalImageHotkeys } from 'app/components/GlobalImageHotkeys';
 import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal';
 import { CanvasPasteModal } from 'features/controlLayers/components/CanvasPasteModal';
-import {
-  NewCanvasSessionDialog,
-  NewGallerySessionDialog,
-} from 'features/controlLayers/components/NewSessionConfirmationAlertDialog';
 import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { DeleteImageModal } from 'features/deleteImageModal/components/DeleteImageModal';
 import { FullscreenDropzone } from 'features/dnd/FullscreenDropzone';
@@ -50,8 +46,6 @@ export const GlobalModalIsolator = memo(() => {
       <RefreshAfterResetModal />
       <DeleteBoardModal />
       <GlobalImageHotkeys />
-      <NewGallerySessionDialog />
-      <NewCanvasSessionDialog />
       <ImageContextMenu />
       <FullscreenDropzone />
       <VideosModal />

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

@@ -317,7 +317,7 @@ const InvokeAIUI = ({
     if (import.meta.env.MODE === 'development') {
       window.$store = $store;
     }
-    () => {
+    return () => {
       $store.set(undefined);
       if (import.meta.env.MODE === 'development') {
         window.$store = undefined;

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

@@ -3,43 +3,39 @@ import 'overlayscrollbars/overlayscrollbars.css';
 import '@xyflow/react/dist/base.css';
 import 'common/components/OverlayScrollbars/overlayscrollbars.css';
 
-import { ChakraProvider, DarkMode, extendTheme, theme as _theme, TOAST_OPTIONS } from '@invoke-ai/ui-library';
+import { ChakraProvider, DarkMode, extendTheme, theme as baseTheme, TOAST_OPTIONS } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
+import { $direction } from 'app/hooks/useSyncLangDirection';
 import type { ReactNode } from 'react';
-import { memo, useEffect, useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
+import { memo, useMemo } from 'react';
 
 type ThemeLocaleProviderProps = {
   children: ReactNode;
 };
 
+const buildTheme = (direction: 'ltr' | 'rtl') => {
+  return extendTheme({
+    ...baseTheme,
+    direction,
+    shadows: {
+      ...baseTheme.shadows,
+      selected:
+        'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-500), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
+      hoverSelected:
+        'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-400), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
+      hoverUnselected:
+        'inset 0px 0px 0px 2px var(--invoke-colors-invokeBlue-300), inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-800)',
+      selectedForCompare:
+        'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-300), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
+      hoverSelectedForCompare:
+        'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-200), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
+    },
+  });
+};
+
 function ThemeLocaleProvider({ children }: ThemeLocaleProviderProps) {
-  const { i18n } = useTranslation();
-
-  const direction = i18n.dir();
-
-  const theme = useMemo(() => {
-    return extendTheme({
-      ..._theme,
-      direction,
-      shadows: {
-        ..._theme.shadows,
-        selected:
-          'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-500), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
-        hoverSelected:
-          'inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-400), inset 0px 0px 0px 4px var(--invoke-colors-invokeBlue-800)',
-        hoverUnselected:
-          'inset 0px 0px 0px 2px var(--invoke-colors-invokeBlue-300), inset 0px 0px 0px 3px var(--invoke-colors-invokeBlue-800)',
-        selectedForCompare:
-          'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-300), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
-        hoverSelectedForCompare:
-          'inset 0px 0px 0px 3px var(--invoke-colors-invokeGreen-200), inset 0px 0px 0px 4px var(--invoke-colors-invokeGreen-800)',
-      },
-    });
-  }, [direction]);
-
-  useEffect(() => {
-    document.body.dir = direction;
-  }, [direction]);
+  const direction = useStore($direction);
+  const theme = useMemo(() => buildTheme(direction), [direction]);
 
   return (
     <ChakraProvider theme={theme} toastOptions={TOAST_OPTIONS}>

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

@@ -21,7 +21,6 @@ import { $isStylePresetsMenuOpen, activeStylePresetIdChanged } from 'features/st
 import { toast } from 'features/toast/toast';
 import { navigationApi } from 'features/ui/layouts/navigation-api';
 import { LAUNCHPAD_PANEL_ID, WORKSPACE_PANEL_ID } from 'features/ui/layouts/shared';
-import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
 import { useLoadWorkflowWithDialog } from 'features/workflowLibrary/components/LoadWorkflowConfirmationAlertDialog';
 import { atom } from 'nanostores';
 import { useCallback, useEffect } from 'react';
@@ -165,7 +164,6 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
           // Go to the generate tab, open the launchpad
           await navigationApi.focusPanel('generate', LAUNCHPAD_PANEL_ID);
           store.dispatch(paramsReset());
-          store.dispatch(activeTabCanvasRightPanelChanged('gallery'));
           break;
         case 'canvas':
           // Go to the canvas tab, open the launchpad

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

@@ -0,0 +1,36 @@
+import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
+import { atom } from 'nanostores';
+import { useEffect } from 'react';
+import { useTranslation } from 'react-i18next';
+
+/**
+ * Global atom storing the language direction, to be consumed by the Chakra theme.
+ *
+ * Why do we need this? We have a kind of catch-22:
+ * - The Chakra theme needs to know the language direction to apply the correct styles.
+ * - The language direction is determined by i18n and the language selection.
+ * - We want our error boundary to be themed.
+ * - It's possible that i18n can throw if the language selection is invalid or not supported.
+ *
+ * Previously, we had the logic in this file in the theme provider, which wrapped the error boundary. The error
+ * was properly themed. But then, if i18n threw in the theme provider, the error boundary does not catch the
+ * error. The app would crash to a white screen.
+ *
+ * We tried swapping the component hierarchy so that the error boundary wraps the theme provider, but then the
+ * error boundary isn't themed!
+ *
+ * The solution is to move this i18n direction logic out of the theme provider and into a hook that we can use
+ * within the error boundary. The error boundary will be themed, _and_ catch any i18n errors.
+ */
+export const $direction = atom<'ltr' | 'rtl'>('ltr');
+
+export const useSyncLangDirection = () => {
+  useAssertSingleton('useSyncLangDirection');
+  const { i18n, t } = useTranslation();
+
+  useEffect(() => {
+    const direction = i18n.dir();
+    $direction.set(direction);
+    document.body.dir = direction;
+  }, [i18n, t]);
+};

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

@@ -2,7 +2,7 @@ import { createLogWriter } from '@roarr/browser-log-writer';
 import { atom } from 'nanostores';
 import type { Logger, MessageSerializer } from 'roarr';
 import { ROARR, Roarr } from 'roarr';
-import { z } from 'zod/v4';
+import { z } from 'zod';
 
 const serializeMessage: MessageSerializer = (message) => {
   return JSON.stringify(message);

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

@@ -1,7 +1,7 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { bboxSyncedToOptimalDimension, rgRefImageModelChanged } from 'features/controlLayers/store/canvasSlice';
-import { selectIsStaging } from 'features/controlLayers/store/canvasStagingAreaSlice';
+import { buildSelectIsStaging, selectCanvasSessionId } from 'features/controlLayers/store/canvasStagingAreaSlice';
 import { loraDeleted } from 'features/controlLayers/store/lorasSlice';
 import { modelChanged, syncedToOptimalDimension, vaeSelected } from 'features/controlLayers/store/paramsSlice';
 import { refImageModelChanged, selectReferenceImageEntities } from 'features/controlLayers/store/refImagesSlice';
@@ -152,7 +152,8 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
       if (modelBase !== state.params.model?.base) {
         // Sync generate tab settings whenever the model base changes
         dispatch(syncedToOptimalDimension());
-        if (!selectIsStaging(state)) {
+        const isStaging = buildSelectIsStaging(selectCanvasSessionId(state))(state);
+        if (!isStaging) {
           // Canvas tab only syncs if not staging
           dispatch(bboxSyncedToOptimalDimension());
         }

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

@@ -15,7 +15,11 @@ import { refImageModelChanged, selectRefImagesSlice } from 'features/controlLaye
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import { getEntityIdentifier, isFLUXReduxConfig, isIPAdapterConfig } from 'features/controlLayers/store/types';
 import { modelSelected } from 'features/parameters/store/actions';
-import { postProcessingModelChanged, upscaleModelChanged } from 'features/parameters/store/upscaleSlice';
+import {
+  postProcessingModelChanged,
+  tileControlnetModelChanged,
+  upscaleModelChanged,
+} from 'features/parameters/store/upscaleSlice';
 import {
   zParameterCLIPEmbedModel,
   zParameterSpandrelImageToImageModel,
@@ -28,6 +32,7 @@ import type { AnyModelConfig } from 'services/api/types';
 import {
   isCLIPEmbedModelConfig,
   isControlLayerModelConfig,
+  isControlNetModelConfig,
   isFluxReduxModelConfig,
   isFluxVAEModelConfig,
   isIPAdapterModelConfig,
@@ -71,6 +76,7 @@ export const addModelsLoadedListener = (startAppListening: AppStartListening) =>
       handleControlAdapterModels(models, state, dispatch, log);
       handlePostProcessingModel(models, state, dispatch, log);
       handleUpscaleModel(models, state, dispatch, log);
+      handleTileControlNetModel(models, state, dispatch, log);
       handleIPAdapterModels(models, state, dispatch, log);
       handleT5EncoderModels(models, state, dispatch, log);
       handleCLIPEmbedModels(models, state, dispatch, log);
@@ -345,6 +351,46 @@ const handleUpscaleModel: ModelHandler = (models, state, dispatch, log) => {
   }
 };
 
+const handleTileControlNetModel: ModelHandler = (models, state, dispatch, log) => {
+  const selectedTileControlNetModel = state.upscale.tileControlnetModel;
+  const controlNetModels = models.filter(isControlNetModelConfig);
+
+  // If the currently selected model is available, we don't need to do anything
+  if (selectedTileControlNetModel && controlNetModels.some((m) => m.key === selectedTileControlNetModel.key)) {
+    return;
+  }
+
+  // The only way we have to identify a model as a tile model is by its name containing 'tile' :)
+  const tileModel = controlNetModels.find((m) => m.name.toLowerCase().includes('tile'));
+
+  // If we have a tile model, select it
+  if (tileModel) {
+    log.debug(
+      { selectedTileControlNetModel, tileModel },
+      'No selected tile ControlNet model or selected model is not available, selecting tile model'
+    );
+    dispatch(tileControlnetModelChanged(tileModel));
+    return;
+  }
+
+  // Otherwise, select the first available ControlNet model
+  const firstModel = controlNetModels[0] || null;
+  if (firstModel) {
+    log.debug(
+      { selectedTileControlNetModel, firstModel },
+      'No tile ControlNet model found, selecting first available ControlNet model'
+    );
+    dispatch(tileControlnetModelChanged(firstModel));
+    return;
+  }
+
+  // No available models, we should clear the selected model - but only if we have one selected
+  if (selectedTileControlNetModel) {
+    log.debug({ selectedTileControlNetModel }, 'Selected tile ControlNet model is not available, clearing');
+    dispatch(tileControlnetModelChanged(null));
+  }
+};
+
 const handleT5EncoderModels: ModelHandler = (models, state, dispatch, log) => {
   const selectedT5EncoderModel = state.params.t5EncoderModel;
   const t5EncoderModels = models.filter((m) => isT5EncoderModelConfig(m));

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

@@ -1,7 +1,7 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { isNil } from 'es-toolkit';
 import { bboxHeightChanged, bboxWidthChanged } from 'features/controlLayers/store/canvasSlice';
-import { selectIsStaging } from 'features/controlLayers/store/canvasStagingAreaSlice';
+import { buildSelectIsStaging, selectCanvasSessionId } from 'features/controlLayers/store/canvasStagingAreaSlice';
 import {
   heightChanged,
   setCfgRescaleMultiplier,
@@ -115,7 +115,8 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
         }
         const setSizeOptions = { updateAspectRatio: true, clamp: true };
 
-        const isStaging = selectIsStaging(getState());
+        const isStaging = buildSelectIsStaging(selectCanvasSessionId(state))(state);
+
         const activeTab = selectActiveTab(getState());
         if (activeTab === 'generate') {
           if (isParameterWidth(width)) {

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

@@ -67,6 +67,8 @@ export type Feature =
   | 'scale'
   | 'creativity'
   | 'structure'
+  | 'tileSize'
+  | 'tileOverlap'
   | 'optimizedDenoising'
   | 'fluxDevLicense';
 

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

@@ -11,9 +11,13 @@ import {
   Text,
 } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
+import { createSelector } from '@reduxjs/toolkit';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
 import { typedMemo } from 'common/util/typedMemo';
 import { NO_DRAG_CLASS, NO_WHEEL_CLASS } from 'features/nodes/types/constants';
+import { selectPickerCompactViewStates } from 'features/ui/store/uiSelectors';
+import { pickerCompactViewStateChanged } from 'features/ui/store/uiSlice';
 import type { AnyStore, ReadableAtom, Task, WritableAtom } from 'nanostores';
 import { atom, computed } from 'nanostores';
 import type { StoreValues } from 'nanostores/computed';
@@ -140,6 +144,10 @@ const NoMatchesFallbackWrapper = typedMemo(({ children }: PropsWithChildren) =>
 NoMatchesFallbackWrapper.displayName = 'NoMatchesFallbackWrapper';
 
 type PickerProps<T extends object> = {
+  /**
+   * Unique identifier for this picker instance. Used to persist compact view state.
+   */
+  pickerId?: string;
   /**
    * The options to display in the picker. This can be a flat array of options or an array of groups.
    */
@@ -204,10 +212,18 @@ type PickerProps<T extends object> = {
   initialGroupStates?: GroupStatusMap;
 };
 
+const buildSelectIsCompactView = (pickerId?: string) =>
+  createSelector([selectPickerCompactViewStates], (compactViewStates) => {
+    if (!pickerId) {
+      return true;
+    }
+    return compactViewStates[pickerId] ?? true;
+  });
+
 export type PickerContextState<T extends object> = {
   $optionsOrGroups: WritableAtom<OptionOrGroup<T>[]>;
   $groupStatusMap: WritableAtom<GroupStatusMap>;
-  $compactView: WritableAtom<boolean>;
+  isCompactView: boolean;
   $activeOptionId: WritableAtom<string | undefined>;
   $filteredOptions: WritableAtom<OptionOrGroup<T>[]>;
   $flattenedFilteredOptions: ReadableAtom<T[]>;
@@ -233,6 +249,7 @@ export type PickerContextState<T extends object> = {
   OptionComponent: React.ComponentType<{ option: T } & BoxProps>;
   NextToSearchBar?: React.ReactNode;
   searchable?: boolean;
+  pickerId?: string;
 };
 
 /* eslint-disable-next-line @typescript-eslint/no-explicit-any */
@@ -503,6 +520,7 @@ const countOptions = <T extends object>(optionsOrGroups: OptionOrGroup<T>[]) =>
 
 export const Picker = typedMemo(<T extends object>(props: PickerProps<T>) => {
   const {
+    pickerId,
     getOptionId,
     optionsOrGroups,
     handleRef,
@@ -521,12 +539,12 @@ export const Picker = typedMemo(<T extends object>(props: PickerProps<T>) => {
   } = props;
   const rootRef = useRef<HTMLDivElement>(null);
   const inputRef = useRef<HTMLInputElement>(null);
+
   const { $groupStatusMap, $areAllGroupsDisabled, toggleGroup } = useTogglableGroups(
     optionsOrGroups,
     initialGroupStates
   );
   const $activeOptionId = useAtom(getFirstOptionId(optionsOrGroups, getOptionId));
-  const $compactView = useAtom(true);
   const $optionsOrGroups = useAtom(optionsOrGroups);
   const $totalOptionCount = useComputed([$optionsOrGroups], countOptions);
   const $filteredOptions = useAtom<OptionOrGroup<T>[]>([]);
@@ -538,6 +556,9 @@ export const Picker = typedMemo(<T extends object>(props: PickerProps<T>) => {
   const $searchTerm = useAtom('');
   const $selectedItemId = useComputed([$selectedItem], (item) => (item ? getOptionId(item) : undefined));
 
+  const selectIsCompactView = useMemo(() => buildSelectIsCompactView(pickerId), [pickerId]);
+  const isCompactView = useAppSelector(selectIsCompactView);
+
   const onSelectById = useCallback(
     (id: string) => {
       const options = $filteredOptions.get();
@@ -565,7 +586,7 @@ export const Picker = typedMemo(<T extends object>(props: PickerProps<T>) => {
       ({
         $optionsOrGroups,
         $groupStatusMap,
-        $compactView,
+        isCompactView,
         $activeOptionId,
         $filteredOptions,
         $flattenedFilteredOptions,
@@ -591,11 +612,12 @@ export const Picker = typedMemo(<T extends object>(props: PickerProps<T>) => {
         $hasOptions,
         $hasFilteredOptions,
         $filteredOptionsCount,
+        pickerId,
       }) satisfies PickerContextState<T>,
     [
       $optionsOrGroups,
       $groupStatusMap,
-      $compactView,
+      isCompactView,
       $activeOptionId,
       $filteredOptions,
       $flattenedFilteredOptions,
@@ -619,6 +641,7 @@ export const Picker = typedMemo(<T extends object>(props: PickerProps<T>) => {
       $hasOptions,
       $hasFilteredOptions,
       $filteredOptionsCount,
+      pickerId,
     ]
   );
 
@@ -869,15 +892,17 @@ GroupToggleButtons.displayName = 'GroupToggleButtons';
 
 const CompactViewToggleButton = typedMemo(<T extends object>() => {
   const { t } = useTranslation();
-  const { $compactView } = usePickerContext<T>();
-  const compactView = useStore($compactView);
+  const dispatch = useAppDispatch();
+  const { isCompactView, pickerId } = usePickerContext<T>();
 
   const onClick = useCallback(() => {
-    $compactView.set(!$compactView.get());
-  }, [$compactView]);
+    if (pickerId) {
+      dispatch(pickerCompactViewStateChanged({ pickerId, isCompact: !isCompactView }));
+    }
+  }, [dispatch, pickerId, isCompactView]);
 
-  const label = compactView ? t('common.fullView') : t('common.compactView');
-  const icon = compactView ? <PiArrowsOutLineVerticalBold /> : <PiArrowsInLineVerticalBold />;
+  const label = isCompactView ? t('common.fullView') : t('common.compactView');
+  const icon = isCompactView ? <PiArrowsOutLineVerticalBold /> : <PiArrowsInLineVerticalBold />;
 
   return <IconButton aria-label={label} tooltip={label} size="sm" variant="ghost" icon={icon} onClick={onClick} />;
 });
@@ -924,8 +949,7 @@ const listSx = {
 } satisfies SystemStyleObject;
 
 const PickerList = typedMemo(<T extends object>() => {
-  const { getOptionId, $compactView, $filteredOptions } = usePickerContext<T>();
-  const compactView = useStore($compactView);
+  const { getOptionId, isCompactView, $filteredOptions } = usePickerContext<T>();
   const filteredOptions = useStore($filteredOptions);
 
   if (filteredOptions.length === 0) {
@@ -934,10 +958,10 @@ const PickerList = typedMemo(<T extends object>() => {
 
   return (
     <ScrollableContent>
-      <Flex sx={listSx} data-is-compact={compactView}>
+      <Flex sx={listSx} data-is-compact={isCompactView}>
         {filteredOptions.map((optionOrGroup, i) => {
           if (isGroup(optionOrGroup)) {
-            const withDivider = !compactView && i < filteredOptions.length - 1;
+            const withDivider = !isCompactView && i < filteredOptions.length - 1;
             return (
               <React.Fragment key={optionOrGroup.id}>
                 <PickerGroup group={optionOrGroup} />
@@ -1079,14 +1103,13 @@ const groupHeaderSx = {
 
 const PickerGroupHeader = typedMemo(<T extends object>({ group }: { group: Group<T> }) => {
   const { t } = useTranslation();
-  const { $compactView } = usePickerContext<T>();
-  const compactView = useStore($compactView);
+  const { isCompactView } = usePickerContext<T>();
   const color = getGroupColor(group);
   const name = getGroupName(group);
   const count = getGroupCount(group, t);
 
   return (
-    <Flex sx={groupHeaderSx} data-is-compact={compactView}>
+    <Flex sx={groupHeaderSx} data-is-compact={isCompactView}>
       <Flex gap={2} alignItems="center">
         <Text fontSize="sm" fontWeight="semibold" color={color} noOfLines={1}>
           {name}

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

@@ -6,7 +6,7 @@ import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
 import { selectIsClientSideUploadEnabled } from 'features/system/store/configSlice';
 import { toast } from 'features/toast/toast';
 import { memo, useCallback } from 'react';
-import type { FileRejection } from 'react-dropzone';
+import type { Accept, FileRejection } from 'react-dropzone';
 import { useDropzone } from 'react-dropzone';
 import { useTranslation } from 'react-i18next';
 import { PiUploadBold } from 'react-icons/pi';
@@ -15,6 +15,18 @@ import type { ImageDTO } from 'services/api/types';
 import { assert } from 'tsafe';
 import type { SetOptional } from 'type-fest';
 
+const addUpperCaseReducer = (acc: string[], ext: string) => {
+  acc.push(ext);
+  acc.push(ext.toUpperCase());
+  return acc;
+};
+
+export const dropzoneAccept: Accept = {
+  'image/png': ['.png'].reduce(addUpperCaseReducer, [] as string[]),
+  'image/jpeg': ['.jpg', '.jpeg', '.png'].reduce(addUpperCaseReducer, [] as string[]),
+  'image/webp': ['.webp'].reduce(addUpperCaseReducer, [] as string[]),
+};
+
 import { useClientSideUpload } from './useClientSideUpload';
 type UseImageUploadButtonArgs =
   | {
@@ -164,11 +176,7 @@ export const useImageUploadButton = ({
     getInputProps: getUploadInputProps,
     open: openUploader,
   } = useDropzone({
-    accept: {
-      'image/png': ['.png'],
-      'image/jpeg': ['.jpg', '.jpeg', '.png'],
-      'image/webp': ['.webp'],
-    },
+    accept: dropzoneAccept,
     onDropAccepted,
     onDropRejected,
     disabled: isDisabled,

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

@@ -1,3 +1,5 @@
-export const preventDefault = (e: React.MouseEvent) => {
+import type { MouseEvent } from 'react';
+
+export const preventDefault = (e: MouseEvent) => {
   e.preventDefault();
 };

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

@@ -1,10 +1,11 @@
 /* eslint-disable @typescript-eslint/no-explicit-any */
+import type React from 'react';
 import { memo } from 'react';
 
 /**
  * A typed version of React.memo, useful for components that take generics.
  */
-export const typedMemo: <T extends keyof JSX.IntrinsicElements | React.JSXElementConstructor<any>>(
+export const typedMemo: <T extends keyof React.JSX.IntrinsicElements | React.JSXElementConstructor<any>>(
   component: T,
   propsAreEqual?: (prevProps: React.ComponentProps<T>, nextProps: React.ComponentProps<T>) => boolean
 ) => T & { displayName?: string } = memo;

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

@@ -1,4 +1,4 @@
-import type { z } from 'zod/v4';
+import type { z } from 'zod';
 
 /**
  * Helper to create a type guard from a zod schema. The type guard will infer the schema's TS type.

invokeai/frontend/web/src/features/changeBoardModal/components/ChangeBoardModal.tsx

@@ -1,7 +1,6 @@
 import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
 import { Combobox, ConfirmationAlertDialog, Flex, FormControl, Text } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
 import {
@@ -14,7 +13,7 @@ import { useTranslation } from 'react-i18next';
 import { useListAllBoardsQuery } from 'services/api/endpoints/boards';
 import { useAddImagesToBoardMutation, useRemoveImagesFromBoardMutation } from 'services/api/endpoints/images';
 
-const selectImagesToChange = createMemoizedSelector(
+const selectImagesToChange = createSelector(
   selectChangeBoardModalSlice,
   (changeBoardModal) => changeBoardModal.image_names
 );

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

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

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

@@ -57,21 +57,21 @@ const CanvasAlertsSelectedEntityStatusContent = memo(({ entityIdentifier, adapte
   const alert = useMemo<AlertData | null>(() => {
     if (isFiltering) {
       return {
-        status: 'info',
+        status: 'warning',
         title: t('controlLayers.HUD.entityStatus.isFiltering', { title }),
       };
     }
 
     if (isTransforming) {
       return {
-        status: 'info',
+        status: 'warning',
         title: t('controlLayers.HUD.entityStatus.isTransforming', { title }),
       };
     }
 
     if (isEmpty) {
       return {
-        status: 'info',
+        status: 'warning',
         title: t('controlLayers.HUD.entityStatus.isEmpty', { title }),
       };
     }

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

@@ -3,6 +3,7 @@ import { EntityListGlobalActionBarAddLayerMenu } from 'features/controlLayers/co
 import { EntityListSelectedEntityActionBarDuplicateButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarDuplicateButton';
 import { EntityListSelectedEntityActionBarFill } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarFill';
 import { EntityListSelectedEntityActionBarFilterButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarFilterButton';
+import { EntityListSelectedEntityActionBarInvertMaskButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarInvertMaskButton';
 import { EntityListSelectedEntityActionBarOpacity } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarOpacity';
 import { EntityListSelectedEntityActionBarSelectObjectButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarSelectObjectButton';
 import { EntityListSelectedEntityActionBarTransformButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarTransformButton';
@@ -21,6 +22,7 @@ export const EntityListSelectedEntityActionBar = memo(() => {
         <EntityListSelectedEntityActionBarSelectObjectButton />
         <EntityListSelectedEntityActionBarFilterButton />
         <EntityListSelectedEntityActionBarTransformButton />
+        <EntityListSelectedEntityActionBarInvertMaskButton />
         <EntityListSelectedEntityActionBarSaveToAssetsButton />
         <EntityListSelectedEntityActionBarDuplicateButton />
         <EntityListNonRasterLayerToggle />

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

@@ -0,0 +1,39 @@
+import { IconButton } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
+import { useInvertMask } from 'features/controlLayers/hooks/useInvertMask';
+import { selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
+import { isInpaintMaskEntityIdentifier } from 'features/controlLayers/store/types';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiSelectionInverseBold } from 'react-icons/pi';
+
+export const EntityListSelectedEntityActionBarInvertMaskButton = memo(() => {
+  const { t } = useTranslation();
+  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
+  const isBusy = useCanvasIsBusy();
+  const invertMask = useInvertMask();
+
+  if (!selectedEntityIdentifier) {
+    return null;
+  }
+
+  if (!isInpaintMaskEntityIdentifier(selectedEntityIdentifier)) {
+    return null;
+  }
+
+  return (
+    <IconButton
+      onClick={invertMask}
+      isDisabled={isBusy}
+      minW={8}
+      variant="link"
+      alignSelf="stretch"
+      aria-label={t('controlLayers.invertMask')}
+      tooltip={t('controlLayers.invertMask')}
+      icon={<PiSelectionInverseBold />}
+    />
+  );
+});
+
+EntityListSelectedEntityActionBarInvertMaskButton.displayName = 'EntityListSelectedEntityActionBarInvertMaskButton';

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

@@ -5,7 +5,6 @@ import { useEntityIdentifierContext } from 'features/controlLayers/contexts/Enti
 import { usePullBboxIntoLayer } from 'features/controlLayers/hooks/saveCanvasHooks';
 import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import { replaceCanvasEntityObjectsWithImage } from 'features/imageActions/actions';
-import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
 import { memo, useCallback, useMemo } from 'react';
 import { Trans } from 'react-i18next';
 import type { ImageDTO } from 'services/api/types';
@@ -21,9 +20,6 @@ export const ControlLayerSettingsEmptyState = memo(() => {
     [dispatch, entityIdentifier, getState]
   );
   const uploadApi = useImageUploadButton({ onUpload, allowMultiple: false });
-  const onClickGalleryButton = useCallback(() => {
-    dispatch(activeTabCanvasRightPanelChanged('gallery'));
-  }, [dispatch]);
   const pullBboxIntoLayer = usePullBboxIntoLayer(entityIdentifier);
 
   const components = useMemo(
@@ -31,14 +27,11 @@ export const ControlLayerSettingsEmptyState = memo(() => {
       UploadButton: (
         <Button isDisabled={isBusy} size="sm" variant="link" color="base.300" {...uploadApi.getUploadButtonProps()} />
       ),
-      GalleryButton: (
-        <Button onClick={onClickGalleryButton} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
-      ),
       PullBboxButton: (
         <Button onClick={pullBboxIntoLayer} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
       ),
     }),
-    [isBusy, onClickGalleryButton, pullBboxIntoLayer, uploadApi]
+    [isBusy, pullBboxIntoLayer, uploadApi]
   );
 
   return (

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

@@ -1,131 +0,0 @@
-import { Checkbox, ConfirmationAlertDialog, Flex, FormControl, FormLabel, Text } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
-import { buildUseBoolean } from 'common/hooks/useBoolean';
-import { canvasSessionReset, generateSessionReset } from 'features/controlLayers/store/canvasStagingAreaSlice';
-import {
-  selectSystemShouldConfirmOnNewSession,
-  shouldConfirmOnNewSessionToggled,
-} from 'features/system/store/systemSlice';
-import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-const [useNewGallerySessionDialog] = buildUseBoolean(false);
-const [useNewCanvasSessionDialog] = buildUseBoolean(false);
-
-const useNewGallerySession = () => {
-  const dispatch = useAppDispatch();
-  const shouldConfirmOnNewSession = useAppSelector(selectSystemShouldConfirmOnNewSession);
-  const newSessionDialog = useNewGallerySessionDialog();
-
-  const newGallerySessionImmediate = useCallback(() => {
-    dispatch(generateSessionReset());
-    dispatch(activeTabCanvasRightPanelChanged('gallery'));
-  }, [dispatch]);
-
-  const newGallerySessionWithDialog = useCallback(() => {
-    if (shouldConfirmOnNewSession) {
-      newSessionDialog.setTrue();
-      return;
-    }
-    newGallerySessionImmediate();
-  }, [newGallerySessionImmediate, newSessionDialog, shouldConfirmOnNewSession]);
-
-  return { newGallerySessionImmediate, newGallerySessionWithDialog };
-};
-
-const useNewCanvasSession = () => {
-  const dispatch = useAppDispatch();
-  const shouldConfirmOnNewSession = useAppSelector(selectSystemShouldConfirmOnNewSession);
-  const newSessionDialog = useNewCanvasSessionDialog();
-
-  const newCanvasSessionImmediate = useCallback(() => {
-    dispatch(canvasSessionReset());
-    dispatch(activeTabCanvasRightPanelChanged('layers'));
-  }, [dispatch]);
-
-  const newCanvasSessionWithDialog = useCallback(() => {
-    if (shouldConfirmOnNewSession) {
-      newSessionDialog.setTrue();
-      return;
-    }
-
-    newCanvasSessionImmediate();
-  }, [newCanvasSessionImmediate, newSessionDialog, shouldConfirmOnNewSession]);
-
-  return { newCanvasSessionImmediate, newCanvasSessionWithDialog };
-};
-
-export const NewGallerySessionDialog = memo(() => {
-  useAssertSingleton('NewGallerySessionDialog');
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-
-  const dialog = useNewGallerySessionDialog();
-  const { newGallerySessionImmediate } = useNewGallerySession();
-
-  const shouldConfirmOnNewSession = useAppSelector(selectSystemShouldConfirmOnNewSession);
-  const onToggleConfirm = useCallback(() => {
-    dispatch(shouldConfirmOnNewSessionToggled());
-  }, [dispatch]);
-
-  return (
-    <ConfirmationAlertDialog
-      isOpen={dialog.isTrue}
-      onClose={dialog.setFalse}
-      title={t('controlLayers.newGallerySession')}
-      acceptCallback={newGallerySessionImmediate}
-      acceptButtonText={t('common.ok')}
-      useInert={false}
-    >
-      <Flex direction="column" gap={3}>
-        <Text>{t('controlLayers.newGallerySessionDesc')}</Text>
-        <Text>{t('common.areYouSure')}</Text>
-        <FormControl>
-          <FormLabel>{t('common.dontAskMeAgain')}</FormLabel>
-          <Checkbox isChecked={!shouldConfirmOnNewSession} onChange={onToggleConfirm} />
-        </FormControl>
-      </Flex>
-    </ConfirmationAlertDialog>
-  );
-});
-
-NewGallerySessionDialog.displayName = 'NewGallerySessionDialog';
-
-export const NewCanvasSessionDialog = memo(() => {
-  useAssertSingleton('NewCanvasSessionDialog');
-  const { t } = useTranslation();
-
-  const dispatch = useAppDispatch();
-
-  const dialog = useNewCanvasSessionDialog();
-  const { newCanvasSessionImmediate } = useNewCanvasSession();
-
-  const shouldConfirmOnNewSession = useAppSelector(selectSystemShouldConfirmOnNewSession);
-  const onToggleConfirm = useCallback(() => {
-    dispatch(shouldConfirmOnNewSessionToggled());
-  }, [dispatch]);
-
-  return (
-    <ConfirmationAlertDialog
-      isOpen={dialog.isTrue}
-      onClose={dialog.setFalse}
-      title={t('controlLayers.newCanvasSession')}
-      acceptCallback={newCanvasSessionImmediate}
-      acceptButtonText={t('common.ok')}
-      useInert={false}
-    >
-      <Flex direction="column" gap={3}>
-        <Text>{t('controlLayers.newCanvasSessionDesc')}</Text>
-        <Text>{t('common.areYouSure')}</Text>
-        <FormControl>
-          <FormLabel>{t('common.dontAskMeAgain')}</FormLabel>
-          <Checkbox isChecked={!shouldConfirmOnNewSession} onChange={onToggleConfirm} />
-        </FormControl>
-      </Flex>
-    </ConfirmationAlertDialog>
-  );
-});
-
-NewCanvasSessionDialog.displayName = 'NewCanvasSessionDialog';

invokeai/frontend/web/src/features/controlLayers/components/RefImage/RefImageList.tsx

@@ -126,6 +126,7 @@ const AddRefImageDropTargetAndButton = memo(() => {
     </Flex>
   );
 });
+AddRefImageDropTargetAndButton.displayName = 'AddRefImageDropTargetAndButton';
 
 const BboxButton = memo(() => {
   const { t } = useTranslation();
@@ -145,4 +146,4 @@ const BboxButton = memo(() => {
     />
   );
 });
-AddRefImageDropTargetAndButton.displayName = 'AddRefImageDropTargetAndButton';
+BboxButton.displayName = 'BboxButton';

invokeai/frontend/web/src/features/controlLayers/components/RefImage/RefImageNoImageState.tsx

@@ -6,7 +6,6 @@ import type { SetGlobalReferenceImageDndTargetData } from 'features/dnd/dnd';
 import { setGlobalReferenceImageDndTarget } from 'features/dnd/dnd';
 import { DndDropTarget } from 'features/dnd/DndDropTarget';
 import { setGlobalReferenceImage } from 'features/imageActions/actions';
-import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
 import { memo, useCallback, useMemo } from 'react';
 import { Trans, useTranslation } from 'react-i18next';
 import type { ImageDTO } from 'services/api/types';
@@ -22,9 +21,6 @@ export const RefImageNoImageState = memo(() => {
     [dispatch, id]
   );
   const uploadApi = useImageUploadButton({ onUpload, allowMultiple: false });
-  const onClickGalleryButton = useCallback(() => {
-    dispatch(activeTabCanvasRightPanelChanged('gallery'));
-  }, [dispatch]);
 
   const dndTargetData = useMemo<SetGlobalReferenceImageDndTargetData>(
     () => setGlobalReferenceImageDndTarget.getData({ id }),
@@ -34,9 +30,8 @@ export const RefImageNoImageState = memo(() => {
   const components = useMemo(
     () => ({
       UploadButton: <Button size="sm" variant="link" color="base.300" {...uploadApi.getUploadButtonProps()} />,
-      GalleryButton: <Button onClick={onClickGalleryButton} size="sm" variant="link" color="base.300" />,
     }),
-    [onClickGalleryButton, uploadApi]
+    [uploadApi]
   );
 
   return (

invokeai/frontend/web/src/features/controlLayers/components/RefImage/RefImageNoImageStateWithCanvasOptions.tsx

@@ -8,7 +8,6 @@ import type { SetGlobalReferenceImageDndTargetData } from 'features/dnd/dnd';
 import { setGlobalReferenceImageDndTarget } from 'features/dnd/dnd';
 import { DndDropTarget } from 'features/dnd/DndDropTarget';
 import { setGlobalReferenceImage } from 'features/imageActions/actions';
-import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
 import { memo, useCallback, useMemo } from 'react';
 import { Trans, useTranslation } from 'react-i18next';
 import type { ImageDTO } from 'services/api/types';
@@ -25,9 +24,6 @@ export const RefImageNoImageStateWithCanvasOptions = memo(() => {
     [dispatch, id]
   );
   const uploadApi = useImageUploadButton({ onUpload, allowMultiple: false });
-  const onClickGalleryButton = useCallback(() => {
-    dispatch(activeTabCanvasRightPanelChanged('gallery'));
-  }, [dispatch]);
   const pullBboxIntoIPAdapter = usePullBboxIntoGlobalReferenceImage(id);
 
   const dndTargetData = useMemo<SetGlobalReferenceImageDndTargetData>(
@@ -40,14 +36,11 @@ export const RefImageNoImageStateWithCanvasOptions = memo(() => {
       UploadButton: (
         <Button isDisabled={isBusy} size="sm" variant="link" color="base.300" {...uploadApi.getUploadButtonProps()} />
       ),
-      GalleryButton: (
-        <Button onClick={onClickGalleryButton} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
-      ),
       PullBboxButton: (
         <Button onClick={pullBboxIntoIPAdapter} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
       ),
     }),
-    [isBusy, onClickGalleryButton, pullBboxIntoIPAdapter, uploadApi]
+    [isBusy, pullBboxIntoIPAdapter, uploadApi]
   );
 
   return (

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

@@ -9,7 +9,6 @@ import type { SetRegionalGuidanceReferenceImageDndTargetData } from 'features/dn
 import { setRegionalGuidanceReferenceImageDndTarget } from 'features/dnd/dnd';
 import { DndDropTarget } from 'features/dnd/DndDropTarget';
 import { setRegionalGuidanceReferenceImage } from 'features/imageActions/actions';
-import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
 import { memo, useCallback, useMemo } from 'react';
 import { Trans, useTranslation } from 'react-i18next';
 import { PiXBold } from 'react-icons/pi';
@@ -31,9 +30,6 @@ export const RegionalGuidanceIPAdapterSettingsEmptyState = memo(({ referenceImag
     [dispatch, entityIdentifier, referenceImageId]
   );
   const uploadApi = useImageUploadButton({ onUpload, allowMultiple: false });
-  const onClickGalleryButton = useCallback(() => {
-    dispatch(activeTabCanvasRightPanelChanged('gallery'));
-  }, [dispatch]);
   const onDeleteIPAdapter = useCallback(() => {
     dispatch(rgRefImageDeleted({ entityIdentifier, referenceImageId }));
   }, [dispatch, entityIdentifier, referenceImageId]);
@@ -53,14 +49,11 @@ export const RegionalGuidanceIPAdapterSettingsEmptyState = memo(({ referenceImag
       UploadButton: (
         <Button isDisabled={isBusy} size="sm" variant="link" color="base.300" {...uploadApi.getUploadButtonProps()} />
       ),
-      GalleryButton: (
-        <Button onClick={onClickGalleryButton} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
-      ),
       PullBboxButton: (
         <Button onClick={pullBboxIntoIPAdapter} isDisabled={isBusy} size="sm" variant="link" color="base.300" />
       ),
     }),
-    [isBusy, onClickGalleryButton, pullBboxIntoIPAdapter, uploadApi]
+    [isBusy, pullBboxIntoIPAdapter, uploadApi]
   );
 
   return (

invokeai/frontend/web/src/features/controlLayers/components/Settings/CanvasSettingsSaveAllImagesToGalleryCheckbox.tsx

@@ -16,7 +16,7 @@ export const CanvasSettingsSaveAllImagesToGalleryCheckbox = memo(() => {
   }, [dispatch]);
   return (
     <FormControl w="full">
-      <FormLabel flexGrow={1}>{t('controlLayers.saveAllImagesToGallery')}</FormLabel>
+      <FormLabel flexGrow={1}>{t('controlLayers.settings.saveAllImagesToGallery.label')}</FormLabel>
       <Checkbox isChecked={saveAllImagesToGallery} onChange={onChange} />
     </FormControl>
   );

invokeai/frontend/web/src/features/controlLayers/components/SimpleSession/context.tsx

@@ -1,585 +0,0 @@
-import { useStore } from '@nanostores/react';
-import { useAppStore } from 'app/store/storeHooks';
-import { getOutputImageName } from 'features/controlLayers/components/SimpleSession/shared';
-import { selectStagingAreaAutoSwitch } from 'features/controlLayers/store/canvasSettingsSlice';
-import {
-  buildSelectSessionQueueItems,
-  canvasQueueItemDiscarded,
-  canvasSessionReset,
-} from 'features/controlLayers/store/canvasStagingAreaSlice';
-import type { ProgressImage } from 'features/nodes/types/common';
-import type { Atom, MapStore, StoreValue, WritableAtom } from 'nanostores';
-import { atom, computed, effect, map, subscribeKeys } from 'nanostores';
-import type { PropsWithChildren } from 'react';
-import { createContext, memo, useCallback, useContext, useEffect, useMemo, useState } from 'react';
-import { getImageDTOSafe } from 'services/api/endpoints/images';
-import { queueApi } from 'services/api/endpoints/queue';
-import type { ImageDTO, S } from 'services/api/types';
-import { $socket } from 'services/events/stores';
-import { assert, objectEntries } from 'tsafe';
-
-export type ProgressData = {
-  itemId: number;
-  progressEvent: S['InvocationProgressEvent'] | null;
-  progressImage: ProgressImage | null;
-  imageDTO: ImageDTO | null;
-  imageLoaded: boolean;
-};
-
-const getInitialProgressData = (itemId: number): ProgressData => ({
-  itemId,
-  progressEvent: null,
-  progressImage: null,
-  imageDTO: null,
-  imageLoaded: false,
-});
-
-export const useProgressData = ($progressData: ProgressDataMap, itemId: number): ProgressData => {
-  const getInitialValue = useCallback(
-    () => $progressData.get()[itemId] ?? getInitialProgressData(itemId),
-    [$progressData, itemId]
-  );
-  const [value, setValue] = useState(getInitialValue);
-  useEffect(() => {
-    const unsub = subscribeKeys($progressData, [itemId], (data) => {
-      const progressData = data[itemId];
-      if (!progressData) {
-        return;
-      }
-      setValue(progressData);
-    });
-    return () => {
-      unsub();
-    };
-  }, [$progressData, itemId]);
-
-  return value;
-};
-
-const setProgress = ($progressData: ProgressDataMap, data: S['InvocationProgressEvent']) => {
-  const progressData = $progressData.get();
-  const current = progressData[data.item_id];
-  if (current) {
-    const next = { ...current };
-    next.progressEvent = data;
-    if (data.image) {
-      next.progressImage = data.image;
-    }
-    $progressData.set({
-      ...progressData,
-      [data.item_id]: next,
-    });
-  } else {
-    $progressData.set({
-      ...progressData,
-      [data.item_id]: {
-        itemId: data.item_id,
-        progressEvent: data,
-        progressImage: data.image ?? null,
-        imageDTO: null,
-        imageLoaded: false,
-      },
-    });
-  }
-};
-
-export type ProgressDataMap = MapStore<Record<number, ProgressData | undefined>>;
-
-type CanvasSessionContextValue = {
-  session: { id: string; type: 'simple' | 'advanced' };
-  $items: Atom<S['SessionQueueItem'][]>;
-  $itemCount: Atom<number>;
-  $hasItems: Atom<boolean>;
-  $isPending: Atom<boolean>;
-  $progressData: ProgressDataMap;
-  $selectedItemId: WritableAtom<number | null>;
-  $selectedItem: Atom<S['SessionQueueItem'] | null>;
-  $selectedItemIndex: Atom<number | null>;
-  $selectedItemOutputImageDTO: Atom<ImageDTO | null>;
-  selectNext: () => void;
-  selectPrev: () => void;
-  selectFirst: () => void;
-  selectLast: () => void;
-  onImageLoad: (itemId: number) => void;
-  discard: (itemId: number) => void;
-  discardAll: () => void;
-};
-
-const CanvasSessionContext = createContext<CanvasSessionContextValue | null>(null);
-
-export const CanvasSessionContextProvider = memo(
-  ({ id, type, children }: PropsWithChildren<{ id: string; type: 'simple' | 'advanced' }>) => {
-    /**
-     * For best performance and interop with the Canvas, which is outside react but needs to interact with the react
-     * app, all canvas session state is packaged as nanostores atoms. The trickiest part is syncing the queue items
-     * with a nanostores atom.
-     */
-    const session = useMemo(() => ({ type, id }), [type, id]);
-
-    /**
-     * App store
-     */
-    const store = useAppStore();
-
-    const socket = useStore($socket);
-
-    /**
-     * Track the last completed item. Used to implement autoswitch.
-     */
-    const $lastCompletedItemId = useState(() => atom<number | null>(null))[0];
-
-    /**
-     * Track the last started item. Used to implement autoswitch.
-     */
-    const $lastStartedItemId = useState(() => atom<number | null>(null))[0];
-
-    /**
-     * Manually-synced atom containing queue items for the current session. This is populated from the RTK Query cache
-     * and kept in sync with it via a redux subscription.
-     */
-    const $items = useState(() => atom<S['SessionQueueItem'][]>([]))[0];
-
-    /**
-     * An internal flag used to work around race conditions with auto-switch switching to queue items before their
-     * output images have fully loaded.
-     */
-    const $lastLoadedItemId = useState(() => atom<number | null>(null))[0];
-
-    /**
-     * An ephemeral store of progress events and images for all items in the current session.
-     */
-    const $progressData = useState(() => map<StoreValue<ProgressDataMap>>({}))[0];
-
-    /**
-     * The currently selected queue item's ID, or null if one is not selected.
-     */
-    const $selectedItemId = useState(() => atom<number | null>(null))[0];
-
-    /**
-     * The number of items. Computed from the queue items array.
-     */
-    const $itemCount = useState(() => computed([$items], (items) => items.length))[0];
-
-    /**
-     * Whether there are any items. Computed from the queue items array.
-     */
-    const $hasItems = useState(() => computed([$items], (items) => items.length > 0))[0];
-
-    /**
-     * Whether there are any pending or in-progress items. Computed from the queue items array.
-     */
-    const $isPending = useState(() =>
-      computed([$items], (items) => items.some((item) => item.status === 'pending' || item.status === 'in_progress'))
-    )[0];
-
-    /**
-     * The currently selected queue item, or null if one is not selected.
-     */
-    const $selectedItem = useState(() =>
-      computed([$items, $selectedItemId], (items, selectedItemId) => {
-        if (items.length === 0) {
-          return null;
-        }
-        if (selectedItemId === null) {
-          return null;
-        }
-        return items.find(({ item_id }) => item_id === selectedItemId) ?? null;
-      })
-    )[0];
-
-    /**
-     * The currently selected queue item's index in the list of items, or null if one is not selected.
-     */
-    const $selectedItemIndex = useState(() =>
-      computed([$items, $selectedItemId], (items, selectedItemId) => {
-        if (items.length === 0) {
-          return null;
-        }
-        if (selectedItemId === null) {
-          return null;
-        }
-        return items.findIndex(({ item_id }) => item_id === selectedItemId) ?? null;
-      })
-    )[0];
-
-    /**
-     * The currently selected queue item's output image name, or null if one is not selected or there is no output
-     * image recorded.
-     */
-    const $selectedItemOutputImageDTO = useState(() =>
-      computed([$selectedItemId, $progressData], (selectedItemId, progressData) => {
-        if (selectedItemId === null) {
-          return null;
-        }
-        const datum = progressData[selectedItemId];
-        if (!datum) {
-          return null;
-        }
-        return datum.imageDTO;
-      })
-    )[0];
-
-    /**
-     * A redux selector to select all queue items from the RTK Query cache.
-     */
-    const selectQueueItems = useMemo(() => buildSelectSessionQueueItems(session.id), [session.id]);
-
-    const discard = useCallback(
-      (itemId: number) => {
-        store.dispatch(canvasQueueItemDiscarded({ itemId }));
-      },
-      [store]
-    );
-
-    const discardAll = useCallback(() => {
-      store.dispatch(canvasSessionReset());
-    }, [store]);
-
-    const selectNext = useCallback(() => {
-      const selectedItemId = $selectedItemId.get();
-      if (selectedItemId === null) {
-        return;
-      }
-      const items = $items.get();
-      const currentIndex = items.findIndex((item) => item.item_id === selectedItemId);
-      const nextIndex = (currentIndex + 1) % items.length;
-      const nextItem = items[nextIndex];
-      if (!nextItem) {
-        return;
-      }
-      $selectedItemId.set(nextItem.item_id);
-    }, [$items, $selectedItemId]);
-
-    const selectPrev = useCallback(() => {
-      const selectedItemId = $selectedItemId.get();
-      if (selectedItemId === null) {
-        return;
-      }
-      const items = $items.get();
-      const currentIndex = items.findIndex((item) => item.item_id === selectedItemId);
-      const prevIndex = (currentIndex - 1 + items.length) % items.length;
-      const prevItem = items[prevIndex];
-      if (!prevItem) {
-        return;
-      }
-      $selectedItemId.set(prevItem.item_id);
-    }, [$items, $selectedItemId]);
-
-    const selectFirst = useCallback(() => {
-      const items = $items.get();
-      const first = items.at(0);
-      if (!first) {
-        return;
-      }
-      $selectedItemId.set(first.item_id);
-    }, [$items, $selectedItemId]);
-
-    const selectLast = useCallback(() => {
-      const items = $items.get();
-      const last = items.at(-1);
-      if (!last) {
-        return;
-      }
-      $selectedItemId.set(last.item_id);
-    }, [$items, $selectedItemId]);
-
-    const onImageLoad = useCallback(
-      (itemId: number) => {
-        const progressData = $progressData.get();
-        const current = progressData[itemId];
-        if (current) {
-          const next = { ...current, imageLoaded: true };
-          $progressData.setKey(itemId, next);
-        } else {
-          $progressData.setKey(itemId, {
-            ...getInitialProgressData(itemId),
-            imageLoaded: true,
-          });
-        }
-        if (
-          $lastCompletedItemId.get() === itemId &&
-          selectStagingAreaAutoSwitch(store.getState()) === 'switch_on_finish'
-        ) {
-          $selectedItemId.set(itemId);
-          $lastCompletedItemId.set(null);
-        }
-      },
-      [$lastCompletedItemId, $progressData, $selectedItemId, store]
-    );
-
-    // Set up socket listeners
-    useEffect(() => {
-      if (!socket) {
-        return;
-      }
-
-      const onProgress = (data: S['InvocationProgressEvent']) => {
-        if (data.destination !== session.id) {
-          return;
-        }
-        setProgress($progressData, data);
-      };
-
-      const onQueueItemStatusChanged = (data: S['QueueItemStatusChangedEvent']) => {
-        if (data.destination !== session.id) {
-          return;
-        }
-        if (data.status === 'completed') {
-          $lastCompletedItemId.set(data.item_id);
-        }
-        if (data.status === 'in_progress') {
-          $lastStartedItemId.set(data.item_id);
-        }
-      };
-
-      socket.on('invocation_progress', onProgress);
-      socket.on('queue_item_status_changed', onQueueItemStatusChanged);
-
-      return () => {
-        socket.off('invocation_progress', onProgress);
-        socket.off('queue_item_status_changed', onQueueItemStatusChanged);
-      };
-    }, [$lastCompletedItemId, $lastStartedItemId, $progressData, $selectedItemId, session.id, socket]);
-
-    // Set up state subscriptions and effects
-    useEffect(() => {
-      let _prevItems: readonly S['SessionQueueItem'][] = [];
-      // Seed the $items atom with the initial query cache state
-      $items.set(selectQueueItems(store.getState()));
-
-      // Manually keep the $items atom in sync as the query cache is updated
-      const unsubReduxSyncToItemsAtom = store.subscribe(() => {
-        const prevItems = $items.get();
-        const items = selectQueueItems(store.getState());
-        if (items !== prevItems) {
-          _prevItems = prevItems;
-          $items.set(items);
-        }
-      });
-
-      // Handle cases that could result in a nonexistent queue item being selected.
-      const unsubEnsureSelectedItemIdExists = effect(
-        [$items, $selectedItemId, $lastStartedItemId],
-        (items, selectedItemId, lastStartedItemId) => {
-          if (items.length === 0) {
-            // If there are no items, cannot have a selected item.
-            $selectedItemId.set(null);
-          } else if (selectedItemId === null && items.length > 0) {
-            // If there is no selected item but there are items, select the first one.
-            $selectedItemId.set(items[0]?.item_id ?? null);
-            return;
-          } else if (
-            selectStagingAreaAutoSwitch(store.getState()) === 'switch_on_start' &&
-            items.findIndex(({ item_id }) => item_id === lastStartedItemId) !== -1
-          ) {
-            $selectedItemId.set(lastStartedItemId);
-            $lastStartedItemId.set(null);
-          } else if (selectedItemId !== null && items.findIndex(({ item_id }) => item_id === selectedItemId) === -1) {
-            // If an item is selected and it is not in the list of items, un-set it. This effect will run again and we'll
-            // the above case, selecting the first item if there are any.
-            let prevIndex = _prevItems.findIndex(({ item_id }) => item_id === selectedItemId);
-            if (prevIndex >= items.length) {
-              prevIndex = items.length - 1;
-            }
-            const nextItem = items[prevIndex];
-            $selectedItemId.set(nextItem?.item_id ?? null);
-          }
-
-          if (items !== _prevItems) {
-            _prevItems = items;
-          }
-        }
-      );
-
-      // Clean up the progress data when a queue item is discarded.
-      const unsubCleanUpProgressData = $items.subscribe(async (items) => {
-        const progressData = $progressData.get();
-
-        const toDelete: number[] = [];
-        const toUpdate: ProgressData[] = [];
-
-        for (const [id, datum] of objectEntries(progressData)) {
-          if (!datum) {
-            toDelete.push(id);
-            continue;
-          }
-          const item = items.find(({ item_id }) => item_id === datum.itemId);
-          if (!item) {
-            toDelete.push(datum.itemId);
-          } else if (item.status === 'canceled' || item.status === 'failed') {
-            toUpdate.push({
-              ...datum,
-              progressEvent: null,
-              progressImage: null,
-              imageDTO: null,
-            });
-          }
-        }
-
-        for (const item of items) {
-          const datum = progressData[item.item_id];
-
-          if (datum) {
-            if (datum.imageDTO) {
-              continue;
-            }
-            const outputImageName = getOutputImageName(item);
-            if (!outputImageName) {
-              continue;
-            }
-            const imageDTO = await getImageDTOSafe(outputImageName);
-            if (!imageDTO) {
-              continue;
-            }
-            toUpdate.push({
-              ...datum,
-              imageDTO,
-            });
-          } else {
-            const outputImageName = getOutputImageName(item);
-            if (!outputImageName) {
-              continue;
-            }
-            const imageDTO = await getImageDTOSafe(outputImageName);
-            if (!imageDTO) {
-              continue;
-            }
-            toUpdate.push({
-              ...getInitialProgressData(item.item_id),
-              imageDTO,
-            });
-          }
-        }
-
-        for (const itemId of toDelete) {
-          $progressData.setKey(itemId, undefined);
-        }
-
-        for (const datum of toUpdate) {
-          $progressData.setKey(datum.itemId, datum);
-        }
-      });
-
-      // We only want to auto-switch to completed queue items once their images have fully loaded to prevent flashes
-      // of fallback content and/or progress images. The only surefire way to determine when images have fully loaded
-      // is via the image elements' `onLoad` callback. Images set `$lastLoadedItemId` to their queue item ID in their
-      // `onLoad` handler, and we listen for that here. If auto-switch is enabled, we then switch the to the item.
-      //
-      // TODO: This isn't perfect... we set $lastLoadedItemId in the mini preview component, but the full view
-      // component still needs to retrieve the image from the browser cache... can result in a flash of the progress
-      // image...
-      const unsubHandleAutoSwitch = $lastLoadedItemId.listen((lastLoadedItemId) => {
-        if (lastLoadedItemId === null) {
-          return;
-        }
-        if (selectStagingAreaAutoSwitch(store.getState()) === 'switch_on_finish') {
-          $selectedItemId.set(lastLoadedItemId);
-        }
-        $lastLoadedItemId.set(null);
-      });
-
-      // Create an RTK Query subscription. Without this, the query cache selector will never return anything bc RTK
-      // doesn't know we care about it.
-      const { unsubscribe: unsubQueueItemsQuery } = store.dispatch(
-        queueApi.endpoints.listAllQueueItems.initiate({ destination: session.id })
-      );
-
-      // const unsubListener = store.dispatch(
-      //   addAppListener({
-      //     matcher: queueApi.endpoints.cancelQueueItem.matchFulfilled,
-      //     effect: ({ payload }, { getState }) => {
-      //       const { item_id } = payload;
-
-      //       const items = selectQueueItems(getState());
-      //       if (items.length === 0) {
-      //         $selectedItemId.set(null);
-      //       } else if ($selectedItemId.get() === null) {
-      //         $selectedItemId.set(items[0].item_id);
-      //       }
-      //     },
-      //   })
-      // );
-
-      // Clean up all subscriptions and top-level (i.e. non-computed/derived state)
-      return () => {
-        unsubHandleAutoSwitch();
-        unsubQueueItemsQuery();
-        unsubReduxSyncToItemsAtom();
-        unsubEnsureSelectedItemIdExists();
-        unsubCleanUpProgressData();
-        $items.set([]);
-        $progressData.set({});
-        $selectedItemId.set(null);
-      };
-    }, [
-      $items,
-      $lastLoadedItemId,
-      $lastStartedItemId,
-      $progressData,
-      $selectedItemId,
-      selectQueueItems,
-      session.id,
-      store,
-    ]);
-
-    const value = useMemo<CanvasSessionContextValue>(
-      () => ({
-        session,
-        $items,
-        $hasItems,
-        $isPending,
-        $progressData,
-        $selectedItemId,
-        $selectedItem,
-        $selectedItemIndex,
-        $selectedItemOutputImageDTO,
-        $itemCount,
-        selectNext,
-        selectPrev,
-        selectFirst,
-        selectLast,
-        onImageLoad,
-        discard,
-        discardAll,
-      }),
-      [
-        $items,
-        $hasItems,
-        $isPending,
-        $progressData,
-        $selectedItem,
-        $selectedItemId,
-        $selectedItemIndex,
-        session,
-        $selectedItemOutputImageDTO,
-        $itemCount,
-        selectNext,
-        selectPrev,
-        selectFirst,
-        selectLast,
-        onImageLoad,
-        discard,
-        discardAll,
-      ]
-    );
-
-    return <CanvasSessionContext.Provider value={value}>{children}</CanvasSessionContext.Provider>;
-  }
-);
-CanvasSessionContextProvider.displayName = 'CanvasSessionContextProvider';
-
-export const useCanvasSessionContext = () => {
-  const ctx = useContext(CanvasSessionContext);
-  assert(ctx !== null, "'useCanvasSessionContext' must be used within a CanvasSessionContextProvider");
-  return ctx;
-};
-
-export const useOutputImageDTO = (item: S['SessionQueueItem']) => {
-  const ctx = useCanvasSessionContext();
-  const $imageDTO = useState(() =>
-    computed([ctx.$progressData], (progressData) => progressData[item.item_id]?.imageDTO ?? null)
-  )[0];
-  const imageDTO = useStore($imageDTO);
-
-  return imageDTO;
-};

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/QueueItemCircularProgress.tsx

@@ -1,10 +1,11 @@
 import type { CircularProgressProps, SystemStyleObject } from '@invoke-ai/ui-library';
 import { CircularProgress, Tooltip } from '@invoke-ai/ui-library';
-import { useCanvasSessionContext, useProgressData } from 'features/controlLayers/components/SimpleSession/context';
-import { getProgressMessage } from 'features/controlLayers/components/SimpleSession/shared';
+import { getProgressMessage } from 'features/controlLayers/components/StagingArea/shared';
 import { memo } from 'react';
 import type { S } from 'services/api/types';
 
+import { useProgressDatum } from './context';
+
 const circleStyles: SystemStyleObject = {
   circle: {
     transitionProperty: 'none',
@@ -18,8 +19,7 @@ const circleStyles: SystemStyleObject = {
 type Props = { itemId: number; status: S['SessionQueueItem']['status'] } & CircularProgressProps;
 
 export const QueueItemCircularProgress = memo(({ itemId, status, ...rest }: Props) => {
-  const { $progressData } = useCanvasSessionContext();
-  const { progressEvent } = useProgressData($progressData, itemId);
+  const { progressEvent } = useProgressDatum(itemId);
 
   if (status !== 'in_progress') {
     return null;

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/QueueItemNumber.tsx

@@ -1,8 +1,9 @@
 import type { TextProps } from '@invoke-ai/ui-library';
 import { Text } from '@invoke-ai/ui-library';
-import { DROP_SHADOW } from 'features/controlLayers/components/SimpleSession/shared';
 import { memo } from 'react';
 
+import { DROP_SHADOW } from './shared';
+
 export const QueueItemNumber = memo(({ number, ...rest }: { number: number } & TextProps) => {
   return <Text pointerEvents="none" userSelect="none" filter={DROP_SHADOW} {...rest}>{`#${number}`}</Text>;
 });

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/QueueItemPreviewMini.tsx

@@ -1,25 +1,23 @@
 import type { SystemStyleObject } from '@invoke-ai/ui-library';
 import { Flex } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import {
-  useCanvasSessionContext,
-  useOutputImageDTO,
-  useProgressData,
-} from 'features/controlLayers/components/SimpleSession/context';
-import { QueueItemCircularProgress } from 'features/controlLayers/components/SimpleSession/QueueItemCircularProgress';
-import { QueueItemNumber } from 'features/controlLayers/components/SimpleSession/QueueItemNumber';
-import { QueueItemProgressImage } from 'features/controlLayers/components/SimpleSession/QueueItemProgressImage';
-import { QueueItemStatusLabel } from 'features/controlLayers/components/SimpleSession/QueueItemStatusLabel';
-import { getQueueItemElementId } from 'features/controlLayers/components/SimpleSession/shared';
+import { QueueItemCircularProgress } from 'features/controlLayers/components/StagingArea/QueueItemCircularProgress';
+import { QueueItemProgressImage } from 'features/controlLayers/components/StagingArea/QueueItemProgressImage';
+import { QueueItemStatusLabel } from 'features/controlLayers/components/StagingArea/QueueItemStatusLabel';
+import { getQueueItemElementId } from 'features/controlLayers/components/StagingArea/shared';
 import {
   selectStagingAreaAutoSwitch,
   settingsStagingAreaAutoSwitchChanged,
 } from 'features/controlLayers/store/canvasSettingsSlice';
 import { DndImage } from 'features/dnd/DndImage';
 import { toast } from 'features/toast/toast';
-import { memo, useCallback } from 'react';
+import { memo, useCallback, useMemo } from 'react';
 import type { S } from 'services/api/types';
 
+import { useOutputImageDTO, useStagingAreaContext } from './context';
+import { QueueItemNumber } from './QueueItemNumber';
+
 const sx = {
   cursor: 'pointer',
   userSelect: 'none',
@@ -41,19 +39,19 @@ const sx = {
 type Props = {
   item: S['SessionQueueItem'];
   index: number;
-  isSelected: boolean;
 };
 
-export const QueueItemPreviewMini = memo(({ item, isSelected, index }: Props) => {
+export const QueueItemPreviewMini = memo(({ item, index }: Props) => {
+  const ctx = useStagingAreaContext();
   const dispatch = useAppDispatch();
-  const ctx = useCanvasSessionContext();
-  const { imageLoaded } = useProgressData(ctx.$progressData, item.item_id);
-  const imageDTO = useOutputImageDTO(item);
+  const $isSelected = useMemo(() => ctx.buildIsSelectedComputed(item.item_id), [ctx, item.item_id]);
+  const isSelected = useStore($isSelected);
+  const imageDTO = useOutputImageDTO(item.item_id);
   const autoSwitch = useAppSelector(selectStagingAreaAutoSwitch);
 
   const onClick = useCallback(() => {
-    ctx.$selectedItemId.set(item.item_id);
-  }, [ctx.$selectedItemId, item.item_id]);
+    ctx.select(item.item_id);
+  }, [ctx, item.item_id]);
 
   const onDoubleClick = useCallback(() => {
     if (autoSwitch !== 'off') {
@@ -65,7 +63,7 @@ export const QueueItemPreviewMini = memo(({ item, isSelected, index }: Props) =>
   }, [autoSwitch, dispatch]);
 
   const onLoad = useCallback(() => {
-    ctx.onImageLoad(item.item_id);
+    ctx.onImageLoaded(item.item_id);
   }, [ctx, item.item_id]);
 
   return (
@@ -77,8 +75,8 @@ export const QueueItemPreviewMini = memo(({ item, isSelected, index }: Props) =>
       onDoubleClick={onDoubleClick}
     >
       <QueueItemStatusLabel item={item} position="absolute" margin="auto" />
-      {imageDTO && <DndImage imageDTO={imageDTO} onLoad={onLoad} asThumbnail position="absolute" />}
-      {!imageLoaded && <QueueItemProgressImage itemId={item.item_id} position="absolute" />}
+      {imageDTO && <DndImage imageDTO={imageDTO} position="absolute" onLoad={onLoad} />}
+      <QueueItemProgressImage itemId={item.item_id} position="absolute" />
       <QueueItemNumber number={index + 1} position="absolute" top={0} left={1} />
       <QueueItemCircularProgress itemId={item.item_id} status={item.status} position="absolute" top={1} right={2} />
     </Flex>

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/QueueItemProgressImage.tsx

@@ -1,15 +1,15 @@
 import type { ImageProps } from '@invoke-ai/ui-library';
 import { Image } from '@invoke-ai/ui-library';
-import { useCanvasSessionContext, useProgressData } from 'features/controlLayers/components/SimpleSession/context';
 import { memo } from 'react';
 
+import { useProgressDatum } from './context';
+
 type Props = { itemId: number } & ImageProps;
 
 export const QueueItemProgressImage = memo(({ itemId, ...rest }: Props) => {
-  const ctx = useCanvasSessionContext();
-  const { progressImage } = useProgressData(ctx.$progressData, itemId);
+  const { progressImage, imageLoaded } = useProgressDatum(itemId);
 
-  if (!progressImage) {
+  if (!progressImage || imageLoaded) {
     return null;
   }
 

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/QueueItemStatusLabel.tsx

@@ -1,16 +1,16 @@
 import type { TextProps } from '@invoke-ai/ui-library';
 import { Text } from '@invoke-ai/ui-library';
-import { useCanvasSessionContext, useProgressData } from 'features/controlLayers/components/SimpleSession/context';
 import { memo } from 'react';
 import type { S } from 'services/api/types';
 
+import { useProgressDatum } from './context';
+
 type Props = { item: S['SessionQueueItem'] } & TextProps;
 
 export const QueueItemStatusLabel = memo(({ item, ...rest }: Props) => {
-  const ctx = useCanvasSessionContext();
-  const { progressImage, imageLoaded } = useProgressData(ctx.$progressData, item.item_id);
+  const { progressImage } = useProgressDatum(item.item_id);
 
-  if (progressImage || imageLoaded) {
+  if (progressImage) {
     return null;
   }
 

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaAutoSwitchButtons.tsx

@@ -1,5 +1,7 @@
 import { IconButton } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import {
   selectStagingAreaAutoSwitch,
   settingsStagingAreaAutoSwitchChanged,
@@ -8,6 +10,9 @@ import { memo, useCallback } from 'react';
 import { PiCaretLineRightBold, PiCaretRightBold, PiMoonBold } from 'react-icons/pi';
 
 export const StagingAreaAutoSwitchButtons = memo(() => {
+  const canvasManager = useCanvasManager();
+  const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
+
   const autoSwitch = useAppSelector(selectStagingAreaAutoSwitch);
   const dispatch = useAppDispatch();
 
@@ -29,6 +34,7 @@ export const StagingAreaAutoSwitchButtons = memo(() => {
         icon={<PiMoonBold />}
         colorScheme={autoSwitch === 'off' ? 'invokeBlue' : 'base'}
         onClick={onClickOff}
+        isDisabled={!shouldShowStagedImage}
       />
       <IconButton
         aria-label="Switch on start"
@@ -36,6 +42,7 @@ export const StagingAreaAutoSwitchButtons = memo(() => {
         icon={<PiCaretRightBold />}
         colorScheme={autoSwitch === 'switch_on_start' ? 'invokeBlue' : 'base'}
         onClick={onClickSwitchOnStart}
+        isDisabled={!shouldShowStagedImage}
       />
       <IconButton
         aria-label="Switch on finish"
@@ -43,6 +50,7 @@ export const StagingAreaAutoSwitchButtons = memo(() => {
         icon={<PiCaretLineRightBold />}
         colorScheme={autoSwitch === 'switch_on_finish' ? 'invokeBlue' : 'base'}
         onClick={onClickSwitchOnFinished}
+        isDisabled={!shouldShowStagedImage}
       />
     </>
   );

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaItemsList.tsx

@@ -1,16 +1,16 @@
 import { Box, Flex, forwardRef } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { logger } from 'app/logging/logger';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
-import { QueueItemPreviewMini } from 'features/controlLayers/components/SimpleSession/QueueItemPreviewMini';
-import { useCanvasManagerSafe } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
+import { QueueItemPreviewMini } from 'features/controlLayers/components/StagingArea/QueueItemPreviewMini';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { useOverlayScrollbars } from 'overlayscrollbars-react';
 import type { CSSProperties, RefObject } from 'react';
-import { memo, useCallback, useEffect, useMemo, useRef, useState } from 'react';
-import type { Components, ItemContent, ListRange, VirtuosoHandle, VirtuosoProps } from 'react-virtuoso';
+import { memo, useCallback, useEffect, useRef, useState } from 'react';
+import type { Components, ComputeItemKey, ItemContent, ListRange, VirtuosoHandle, VirtuosoProps } from 'react-virtuoso';
 import { Virtuoso } from 'react-virtuoso';
 import type { S } from 'services/api/types';
 
+import { useStagingAreaContext } from './context';
 import { getQueueItemElementId } from './shared';
 
 const log = logger('system');
@@ -20,8 +20,6 @@ const virtuosoStyles = {
   height: '72px',
 } satisfies CSSProperties;
 
-type VirtuosoContext = { selectedItemId: number | null };
-
 /**
  * Scroll the item at the given index into view if it is not currently visible.
  */
@@ -132,28 +130,26 @@ const useScrollableStagingArea = (rootRef: RefObject<HTMLDivElement>) => {
 };
 
 export const StagingAreaItemsList = memo(() => {
-  const canvasManager = useCanvasManagerSafe();
-  const ctx = useCanvasSessionContext();
+  const canvasManager = useCanvasManager();
+
+  const ctx = useStagingAreaContext();
   const virtuosoRef = useRef<VirtuosoHandle>(null);
   const rangeRef = useRef<ListRange>({ startIndex: 0, endIndex: 0 });
   const rootRef = useRef<HTMLDivElement>(null);
 
   const items = useStore(ctx.$items);
-  const selectedItemId = useStore(ctx.$selectedItemId);
 
-  const context = useMemo(() => ({ selectedItemId }), [selectedItemId]);
   const scrollerRef = useScrollableStagingArea(rootRef);
 
   useEffect(() => {
-    if (!canvasManager) {
-      return;
-    }
-
-    return canvasManager.stagingArea.connectToSession(ctx.$selectedItemId, ctx.$progressData, ctx.$isPending);
-  }, [canvasManager, ctx.$progressData, ctx.$selectedItemId, ctx.$isPending]);
+    return canvasManager.stagingArea.connectToSession(ctx.$items, ctx.$selectedItem);
+  }, [canvasManager, ctx.$progressData, ctx.$items, ctx.$selectedItem]);
 
   useEffect(() => {
-    return ctx.$selectedItemIndex.listen((index) => {
+    return ctx.$selectedItemIndex.listen((selectedItemIndex) => {
+      if (selectedItemIndex === null) {
+        return;
+      }
       if (!virtuosoRef.current) {
         return;
       }
@@ -162,11 +158,7 @@ export const StagingAreaItemsList = memo(() => {
         return;
       }
 
-      if (index === null) {
-        return;
-      }
-
-      scrollIntoView(index, rootRef.current, virtuosoRef.current, rangeRef.current);
+      scrollIntoView(selectedItemIndex, rootRef.current, virtuosoRef.current, rangeRef.current);
     });
   }, [ctx.$selectedItemIndex]);
 
@@ -176,40 +168,46 @@ export const StagingAreaItemsList = memo(() => {
 
   return (
     <Box data-overlayscrollbars-initialize="" ref={rootRef} position="relative" w="full" h="full">
-      <Virtuoso<S['SessionQueueItem'], VirtuosoContext>
+      <Virtuoso<S['SessionQueueItem']>
         ref={virtuosoRef}
-        context={context}
         data={items}
         horizontalDirection
         style={virtuosoStyles}
+        computeItemKey={computeItemKey}
+        increaseViewportBy={2048}
         itemContent={itemContent}
         components={components}
         rangeChanged={onRangeChanged}
         // Virtuoso expects the ref to be of HTMLElement | null | Window, but overlayscrollbars doesn't allow Window
-        scrollerRef={scrollerRef as VirtuosoProps<S['SessionQueueItem'], VirtuosoContext>['scrollerRef']}
+        scrollerRef={scrollerRef as VirtuosoProps<S['SessionQueueItem'], void>['scrollerRef']}
       />
     </Box>
   );
 });
 StagingAreaItemsList.displayName = 'StagingAreaItemsList';
 
-const itemContent: ItemContent<S['SessionQueueItem'], VirtuosoContext> = (index, item, { selectedItemId }) => (
-  <QueueItemPreviewMini
-    key={`${item.item_id}-mini`}
-    item={item}
-    index={index}
-    isSelected={selectedItemId === item.item_id}
-  />
+const computeItemKey: ComputeItemKey<S['SessionQueueItem'], void> = (_, item: S['SessionQueueItem']) => {
+  return item.item_id;
+};
+
+const itemContent: ItemContent<S['SessionQueueItem'], void> = (index, item) => (
+  <QueueItemPreviewMini key={`${item.item_id}-mini`} item={item} index={index} />
 );
 
 const listSx = {
   '& > * + *': {
     pl: 2,
   },
+  '&[data-disabled="true"]': {
+    filter: 'grayscale(1) opacity(0.5)',
+  },
 };
 
-const components: Components<S['SessionQueueItem'], VirtuosoContext> = {
+const components: Components<S['SessionQueueItem']> = {
   List: forwardRef(({ context: _, ...rest }, ref) => {
-    return <Flex ref={ref} sx={listSx} {...rest} />;
+    const canvasManager = useCanvasManager();
+    const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
+
+    return <Flex ref={ref} sx={listSx} data-disabled={!shouldShowStagedImage} {...rest} />;
   }),
 };

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbar.tsx

@@ -1,6 +1,5 @@
 import { ButtonGroup, Flex } from '@invoke-ai/ui-library';
-import { useStore } from '@nanostores/react';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
+import { useStagingAreaContext } from 'features/controlLayers/components/StagingArea/context';
 import { StagingAreaToolbarAcceptButton } from 'features/controlLayers/components/StagingArea/StagingAreaToolbarAcceptButton';
 import { StagingAreaToolbarDiscardAllButton } from 'features/controlLayers/components/StagingArea/StagingAreaToolbarDiscardAllButton';
 import { StagingAreaToolbarDiscardSelectedButton } from 'features/controlLayers/components/StagingArea/StagingAreaToolbarDiscardSelectedButton';
@@ -10,17 +9,13 @@ import { StagingAreaToolbarNextButton } from 'features/controlLayers/components/
 import { StagingAreaToolbarPrevButton } from 'features/controlLayers/components/StagingArea/StagingAreaToolbarPrevButton';
 import { StagingAreaToolbarSaveSelectedToGalleryButton } from 'features/controlLayers/components/StagingArea/StagingAreaToolbarSaveSelectedToGalleryButton';
 import { StagingAreaToolbarToggleShowResultsButton } from 'features/controlLayers/components/StagingArea/StagingAreaToolbarToggleShowResultsButton';
-import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { memo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 
 import { StagingAreaAutoSwitchButtons } from './StagingAreaAutoSwitchButtons';
 
 export const StagingAreaToolbar = memo(() => {
-  const canvasManager = useCanvasManager();
-  const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
-
-  const ctx = useCanvasSessionContext();
+  const ctx = useStagingAreaContext();
 
   useHotkeys('meta+left', ctx.selectFirst, { preventDefault: true });
   useHotkeys('meta+right', ctx.selectLast, { preventDefault: true });
@@ -28,22 +23,22 @@ export const StagingAreaToolbar = memo(() => {
   return (
     <Flex gap={2}>
       <ButtonGroup borderRadius="base" shadow="dark-lg">
-        <StagingAreaToolbarPrevButton isDisabled={!shouldShowStagedImage} />
+        <StagingAreaToolbarPrevButton />
         <StagingAreaToolbarImageCountButton />
-        <StagingAreaToolbarNextButton isDisabled={!shouldShowStagedImage} />
+        <StagingAreaToolbarNextButton />
       </ButtonGroup>
       <ButtonGroup borderRadius="base" shadow="dark-lg">
         <StagingAreaToolbarAcceptButton />
         <StagingAreaToolbarToggleShowResultsButton />
         <StagingAreaToolbarSaveSelectedToGalleryButton />
         <StagingAreaToolbarMenu />
-        <StagingAreaToolbarDiscardSelectedButton isDisabled={!shouldShowStagedImage} />
+        <StagingAreaToolbarDiscardSelectedButton />
       </ButtonGroup>
       <ButtonGroup borderRadius="base" shadow="dark-lg">
         <StagingAreaAutoSwitchButtons />
       </ButtonGroup>
       <ButtonGroup borderRadius="base" shadow="dark-lg">
-        <StagingAreaToolbarDiscardAllButton isDisabled={!shouldShowStagedImage} />
+        <StagingAreaToolbarDiscardAllButton />
       </ButtonGroup>
     </Flex>
   );

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarAcceptButton.tsx

@@ -1,64 +1,32 @@
 import { IconButton } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { useIsRegionFocused } from 'common/hooks/focus';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
+import { useStagingAreaContext } from 'features/controlLayers/components/StagingArea/context';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
-import { canvasSessionReset } from 'features/controlLayers/store/canvasStagingAreaSlice';
-import { selectBboxRect, selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
-import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
-import { imageNameToImageObject } from 'features/controlLayers/store/util';
 import { useCancelQueueItemsByDestination } from 'features/queue/hooks/useCancelQueueItemsByDestination';
-import { memo, useCallback } from 'react';
+import { memo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiCheckBold } from 'react-icons/pi';
 
 export const StagingAreaToolbarAcceptButton = memo(() => {
-  const ctx = useCanvasSessionContext();
-  const dispatch = useAppDispatch();
+  const ctx = useStagingAreaContext();
   const canvasManager = useCanvasManager();
-  const bboxRect = useAppSelector(selectBboxRect);
-  const selectedEntityIdentifier = useAppSelector(selectSelectedEntityIdentifier);
   const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
   const isCanvasFocused = useIsRegionFocused('canvas');
-  const selectedItemImageDTO = useStore(ctx.$selectedItemOutputImageDTO);
   const cancelQueueItemsByDestination = useCancelQueueItemsByDestination();
+  const acceptSelectedIsEnabled = useStore(ctx.$acceptSelectedIsEnabled);
 
   const { t } = useTranslation();
 
-  const acceptSelected = useCallback(() => {
-    if (!selectedItemImageDTO) {
-      return;
-    }
-    const { x, y, width, height } = bboxRect;
-    const imageObject = imageNameToImageObject(selectedItemImageDTO.image_name, { width, height });
-    const overrides: Partial<CanvasRasterLayerState> = {
-      position: { x, y },
-      objects: [imageObject],
-    };
-
-    dispatch(rasterLayerAdded({ overrides, isSelected: selectedEntityIdentifier?.type === 'raster_layer' }));
-    dispatch(canvasSessionReset());
-    cancelQueueItemsByDestination.trigger(ctx.session.id, { withToast: false });
-  }, [
-    selectedItemImageDTO,
-    bboxRect,
-    dispatch,
-    selectedEntityIdentifier?.type,
-    cancelQueueItemsByDestination,
-    ctx.session.id,
-  ]);
-
   useHotkeys(
     ['enter'],
-    acceptSelected,
+    ctx.acceptSelected,
     {
       preventDefault: true,
-      enabled: isCanvasFocused && shouldShowStagedImage && selectedItemImageDTO !== null,
+      enabled: isCanvasFocused && shouldShowStagedImage && acceptSelectedIsEnabled,
     },
-    [isCanvasFocused, shouldShowStagedImage, selectedItemImageDTO]
+    [ctx.acceptSelected, isCanvasFocused, shouldShowStagedImage, acceptSelectedIsEnabled]
   );
 
   return (
@@ -66,9 +34,9 @@ export const StagingAreaToolbarAcceptButton = memo(() => {
       tooltip={`${t('common.accept')} (Enter)`}
       aria-label={`${t('common.accept')} (Enter)`}
       icon={<PiCheckBold />}
-      onClick={acceptSelected}
+      onClick={ctx.acceptSelected}
       colorScheme="invokeBlue"
-      isDisabled={!selectedItemImageDTO || !shouldShowStagedImage || cancelQueueItemsByDestination.isDisabled}
+      isDisabled={!acceptSelectedIsEnabled || !shouldShowStagedImage || cancelQueueItemsByDestination.isDisabled}
       isLoading={cancelQueueItemsByDestination.isLoading}
     />
   );

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarDiscardAllButton.tsx

@@ -1,28 +1,28 @@
 import { IconButton } from '@invoke-ai/ui-library';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
+import { useStore } from '@nanostores/react';
+import { useStagingAreaContext } from 'features/controlLayers/components/StagingArea/context';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { useCancelQueueItemsByDestination } from 'features/queue/hooks/useCancelQueueItemsByDestination';
-import { memo, useCallback } from 'react';
+import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiTrashSimpleBold } from 'react-icons/pi';
 
-export const StagingAreaToolbarDiscardAllButton = memo(({ isDisabled }: { isDisabled?: boolean }) => {
-  const ctx = useCanvasSessionContext();
+export const StagingAreaToolbarDiscardAllButton = memo(() => {
+  const canvasManager = useCanvasManager();
+  const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
+
+  const ctx = useStagingAreaContext();
   const { t } = useTranslation();
   const cancelQueueItemsByDestination = useCancelQueueItemsByDestination();
 
-  const discardAll = useCallback(() => {
-    ctx.discardAll();
-    cancelQueueItemsByDestination.trigger(ctx.session.id, { withToast: false });
-  }, [cancelQueueItemsByDestination, ctx]);
-
   return (
     <IconButton
       tooltip={`${t('controlLayers.stagingArea.discardAll')} (Esc)`}
       aria-label={t('controlLayers.stagingArea.discardAll')}
       icon={<PiTrashSimpleBold />}
-      onClick={discardAll}
+      onClick={ctx.discardAll}
       colorScheme="error"
-      isDisabled={isDisabled || cancelQueueItemsByDestination.isDisabled}
+      isDisabled={cancelQueueItemsByDestination.isDisabled || !shouldShowStagedImage}
       isLoading={cancelQueueItemsByDestination.isLoading}
     />
   );

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarDiscardSelectedButton.tsx

@@ -1,34 +1,30 @@
 import { IconButton } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
+import { useStagingAreaContext } from 'features/controlLayers/components/StagingArea/context';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { useCancelQueueItem } from 'features/queue/hooks/useCancelQueueItem';
-import { memo, useCallback } from 'react';
+import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiXBold } from 'react-icons/pi';
 
-export const StagingAreaToolbarDiscardSelectedButton = memo(({ isDisabled }: { isDisabled?: boolean }) => {
-  const ctx = useCanvasSessionContext();
+export const StagingAreaToolbarDiscardSelectedButton = memo(() => {
+  const canvasManager = useCanvasManager();
+  const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
+
+  const ctx = useStagingAreaContext();
   const cancelQueueItem = useCancelQueueItem();
-  const selectedItemId = useStore(ctx.$selectedItemId);
+  const discardSelectedIsEnabled = useStore(ctx.$discardSelectedIsEnabled);
 
   const { t } = useTranslation();
 
-  const discardSelected = useCallback(async () => {
-    if (selectedItemId === null) {
-      return;
-    }
-    ctx.discard(selectedItemId);
-    await cancelQueueItem.trigger(selectedItemId, { withToast: false });
-  }, [selectedItemId, ctx, cancelQueueItem]);
-
   return (
     <IconButton
       tooltip={t('controlLayers.stagingArea.discard')}
       aria-label={t('controlLayers.stagingArea.discard')}
       icon={<PiXBold />}
-      onClick={discardSelected}
+      onClick={ctx.discardSelected}
       colorScheme="invokeBlue"
-      isDisabled={selectedItemId === null || cancelQueueItem.isDisabled || isDisabled}
+      isDisabled={!discardSelectedIsEnabled || cancelQueueItem.isDisabled || !shouldShowStagedImage}
       isLoading={cancelQueueItem.isLoading}
     />
   );

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarImageCountButton.tsx

@@ -1,23 +1,27 @@
 import { Button } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
+import { useStagingAreaContext } from 'features/controlLayers/components/StagingArea/context';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { memo, useMemo } from 'react';
 
 export const StagingAreaToolbarImageCountButton = memo(() => {
-  const ctx = useCanvasSessionContext();
-  const selectItemIndex = useStore(ctx.$selectedItemIndex);
+  const canvasManager = useCanvasManager();
+  const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
+
+  const ctx = useStagingAreaContext();
+  const selectedItem = useStore(ctx.$selectedItem);
   const itemCount = useStore(ctx.$itemCount);
 
   const counterText = useMemo(() => {
-    if (itemCount > 0 && selectItemIndex !== null) {
-      return `${selectItemIndex + 1} of ${itemCount}`;
+    if (itemCount > 0 && selectedItem !== null) {
+      return `${selectedItem.index + 1} of ${itemCount}`;
     } else {
       return `0 of 0`;
     }
-  }, [itemCount, selectItemIndex]);
+  }, [itemCount, selectedItem]);
 
   return (
-    <Button colorScheme="base" pointerEvents="none" minW={28}>
+    <Button colorScheme="base" pointerEvents="none" minW={28} isDisabled={!shouldShowStagedImage}>
       {counterText}
     </Button>
   );

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarMenu.tsx

@@ -1,12 +1,23 @@
 import { IconButton, Menu, MenuButton, MenuList } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
 import { StagingAreaToolbarNewLayerFromImageMenuItems } from 'features/controlLayers/components/StagingArea/StagingAreaToolbarMenuNewLayerFromImage';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { memo } from 'react';
 import { PiDotsThreeVerticalBold } from 'react-icons/pi';
 
 export const StagingAreaToolbarMenu = memo(() => {
+  const canvasManager = useCanvasManager();
+  const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
+
   return (
     <Menu>
-      <MenuButton as={IconButton} icon={<PiDotsThreeVerticalBold />} colorScheme="invokeBlue" />
+      <MenuButton
+        tooltip="Image Actions"
+        as={IconButton}
+        icon={<PiDotsThreeVerticalBold />}
+        colorScheme="invokeBlue"
+        isDisabled={!shouldShowStagedImage}
+      />
       <MenuList>
         <StagingAreaToolbarNewLayerFromImageMenuItems />
       </MenuList>

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarMenuNewLayerFromImage.tsx

@@ -2,7 +2,7 @@ import { MenuGroup, MenuItem } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useAppStore } from 'app/store/storeHooks';
 import { NewLayerIcon } from 'features/controlLayers/components/common/icons';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
+import { useStagingAreaContext } from 'features/controlLayers/components/StagingArea/context';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { createNewCanvasEntityFromImage } from 'features/imageActions/actions';
 import { toast } from 'features/toast/toast';
@@ -15,8 +15,8 @@ const uploadImageArg = { image_category: 'general', is_intermediate: true, silen
 export const StagingAreaToolbarNewLayerFromImageMenuItems = memo(() => {
   const canvasManager = useCanvasManager();
   const { t } = useTranslation();
-  const ctx = useCanvasSessionContext();
-  const selectedItemOutputImageDTO = useStore(ctx.$selectedItemOutputImageDTO);
+  const ctx = useStagingAreaContext();
+  const selectedItemImageDTO = useStore(ctx.$selectedItemImageDTO);
   const store = useAppStore();
   const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
 
@@ -29,11 +29,11 @@ export const StagingAreaToolbarNewLayerFromImageMenuItems = memo(() => {
   }, [t]);
 
   const onClickNewRasterLayerFromImage = useCallback(async () => {
-    if (!selectedItemOutputImageDTO) {
+    if (!selectedItemImageDTO) {
       return;
     }
     const { dispatch, getState } = store;
-    const imageDTO = await copyImage(selectedItemOutputImageDTO.image_name, uploadImageArg);
+    const imageDTO = await copyImage(selectedItemImageDTO.image_name, uploadImageArg);
     createNewCanvasEntityFromImage({
       imageDTO,
       type: 'raster_layer',
@@ -42,14 +42,14 @@ export const StagingAreaToolbarNewLayerFromImageMenuItems = memo(() => {
       overrides: { isEnabled: false }, // We are adding the layer while staging, it should be disabled by default
     });
     toastSentToCanvas();
-  }, [selectedItemOutputImageDTO, store, toastSentToCanvas]);
+  }, [selectedItemImageDTO, store, toastSentToCanvas]);
 
   const onClickNewControlLayerFromImage = useCallback(async () => {
-    if (!selectedItemOutputImageDTO) {
+    if (!selectedItemImageDTO) {
       return;
     }
     const { dispatch, getState } = store;
-    const imageDTO = await copyImage(selectedItemOutputImageDTO.image_name, uploadImageArg);
+    const imageDTO = await copyImage(selectedItemImageDTO.image_name, uploadImageArg);
 
     createNewCanvasEntityFromImage({
       imageDTO,
@@ -59,14 +59,14 @@ export const StagingAreaToolbarNewLayerFromImageMenuItems = memo(() => {
       overrides: { isEnabled: false }, // We are adding the layer while staging, it should be disabled by default
     });
     toastSentToCanvas();
-  }, [selectedItemOutputImageDTO, store, toastSentToCanvas]);
+  }, [selectedItemImageDTO, store, toastSentToCanvas]);
 
   const onClickNewInpaintMaskFromImage = useCallback(async () => {
-    if (!selectedItemOutputImageDTO) {
+    if (!selectedItemImageDTO) {
       return;
     }
     const { dispatch, getState } = store;
-    const imageDTO = await copyImage(selectedItemOutputImageDTO.image_name, uploadImageArg);
+    const imageDTO = await copyImage(selectedItemImageDTO.image_name, uploadImageArg);
 
     createNewCanvasEntityFromImage({
       imageDTO,
@@ -76,14 +76,14 @@ export const StagingAreaToolbarNewLayerFromImageMenuItems = memo(() => {
       overrides: { isEnabled: false }, // We are adding the layer while staging, it should be disabled by default
     });
     toastSentToCanvas();
-  }, [selectedItemOutputImageDTO, store, toastSentToCanvas]);
+  }, [selectedItemImageDTO, store, toastSentToCanvas]);
 
   const onClickNewRegionalGuidanceFromImage = useCallback(async () => {
-    if (!selectedItemOutputImageDTO) {
+    if (!selectedItemImageDTO) {
       return;
     }
     const { dispatch, getState } = store;
-    const imageDTO = await copyImage(selectedItemOutputImageDTO.image_name, uploadImageArg);
+    const imageDTO = await copyImage(selectedItemImageDTO.image_name, uploadImageArg);
 
     createNewCanvasEntityFromImage({
       imageDTO,
@@ -93,35 +93,35 @@ export const StagingAreaToolbarNewLayerFromImageMenuItems = memo(() => {
       overrides: { isEnabled: false }, // We are adding the layer while staging, it should be disabled by default
     });
     toastSentToCanvas();
-  }, [selectedItemOutputImageDTO, store, toastSentToCanvas]);
+  }, [selectedItemImageDTO, store, toastSentToCanvas]);
 
   return (
     <MenuGroup title="New Layer From Image">
       <MenuItem
         icon={<NewLayerIcon />}
         onClickCapture={onClickNewInpaintMaskFromImage}
-        isDisabled={!selectedItemOutputImageDTO || !shouldShowStagedImage}
+        isDisabled={!selectedItemImageDTO || !shouldShowStagedImage}
       >
         {t('controlLayers.inpaintMask')}
       </MenuItem>
       <MenuItem
         icon={<NewLayerIcon />}
         onClickCapture={onClickNewRegionalGuidanceFromImage}
-        isDisabled={!selectedItemOutputImageDTO || !shouldShowStagedImage}
+        isDisabled={!selectedItemImageDTO || !shouldShowStagedImage}
       >
         {t('controlLayers.regionalGuidance')}
       </MenuItem>
       <MenuItem
         icon={<NewLayerIcon />}
         onClickCapture={onClickNewControlLayerFromImage}
-        isDisabled={!selectedItemOutputImageDTO || !shouldShowStagedImage}
+        isDisabled={!selectedItemImageDTO || !shouldShowStagedImage}
       >
         {t('controlLayers.controlLayer')}
       </MenuItem>
       <MenuItem
         icon={<NewLayerIcon />}
         onClickCapture={onClickNewRasterLayerFromImage}
-        isDisabled={!selectedItemOutputImageDTO || !shouldShowStagedImage}
+        isDisabled={!selectedItemImageDTO || !shouldShowStagedImage}
       >
         {t('controlLayers.rasterLayer')}
       </MenuItem>

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarNextButton.tsx

@@ -1,14 +1,18 @@
 import { IconButton } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useIsRegionFocused } from 'common/hooks/focus';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
+import { useStagingAreaContext } from 'features/controlLayers/components/StagingArea/context';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { memo, useCallback } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiArrowRightBold } from 'react-icons/pi';
 
-export const StagingAreaToolbarNextButton = memo(({ isDisabled }: { isDisabled?: boolean }) => {
-  const ctx = useCanvasSessionContext();
+export const StagingAreaToolbarNextButton = memo(() => {
+  const canvasManager = useCanvasManager();
+  const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
+
+  const ctx = useStagingAreaContext();
   const itemCount = useStore(ctx.$itemCount);
   const isCanvasFocused = useIsRegionFocused('canvas');
 
@@ -23,9 +27,9 @@ export const StagingAreaToolbarNextButton = memo(({ isDisabled }: { isDisabled?:
     ctx.selectNext,
     {
       preventDefault: true,
-      enabled: isCanvasFocused && !isDisabled && itemCount > 1,
+      enabled: isCanvasFocused && shouldShowStagedImage && itemCount > 1,
     },
-    [isCanvasFocused, isDisabled, itemCount, ctx.selectNext]
+    [isCanvasFocused, shouldShowStagedImage, itemCount, ctx.selectNext]
   );
 
   return (
@@ -35,7 +39,7 @@ export const StagingAreaToolbarNextButton = memo(({ isDisabled }: { isDisabled?:
       icon={<PiArrowRightBold />}
       onClick={selectNext}
       colorScheme="invokeBlue"
-      isDisabled={itemCount <= 1 || isDisabled}
+      isDisabled={itemCount <= 1 || !shouldShowStagedImage}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarPrevButton.tsx

@@ -1,14 +1,17 @@
 import { IconButton } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useIsRegionFocused } from 'common/hooks/focus';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
+import { useStagingAreaContext } from 'features/controlLayers/components/StagingArea/context';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { memo, useCallback } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import { PiArrowLeftBold } from 'react-icons/pi';
 
-export const StagingAreaToolbarPrevButton = memo(({ isDisabled }: { isDisabled?: boolean }) => {
-  const ctx = useCanvasSessionContext();
+export const StagingAreaToolbarPrevButton = memo(() => {
+  const canvasManager = useCanvasManager();
+  const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
+  const ctx = useStagingAreaContext();
   const itemCount = useStore(ctx.$itemCount);
   const isCanvasFocused = useIsRegionFocused('canvas');
 
@@ -23,9 +26,9 @@ export const StagingAreaToolbarPrevButton = memo(({ isDisabled }: { isDisabled?:
     ctx.selectPrev,
     {
       preventDefault: true,
-      enabled: isCanvasFocused && !isDisabled && itemCount > 1,
+      enabled: isCanvasFocused && shouldShowStagedImage && itemCount > 1,
     },
-    [isCanvasFocused, isDisabled, itemCount, ctx.selectPrev]
+    [isCanvasFocused, shouldShowStagedImage, itemCount, ctx.selectPrev]
   );
 
   return (
@@ -35,7 +38,7 @@ export const StagingAreaToolbarPrevButton = memo(({ isDisabled }: { isDisabled?:
       icon={<PiArrowLeftBold />}
       onClick={selectPrev}
       colorScheme="invokeBlue"
-      isDisabled={itemCount <= 1 || isDisabled}
+      isDisabled={itemCount <= 1 || !shouldShowStagedImage}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/StagingAreaToolbarSaveSelectedToGalleryButton.tsx

@@ -2,7 +2,7 @@ import { IconButton } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useAppSelector } from 'app/store/storeHooks';
 import { withResultAsync } from 'common/util/result';
-import { useCanvasSessionContext } from 'features/controlLayers/components/SimpleSession/context';
+import { useStagingAreaContext } from 'features/controlLayers/components/StagingArea/context';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
 import { selectAutoAddBoardId } from 'features/gallery/store/gallerySelectors';
 import { toast } from 'features/toast/toast';
@@ -16,14 +16,14 @@ const TOAST_ID = 'SAVE_STAGING_AREA_IMAGE_TO_GALLERY';
 export const StagingAreaToolbarSaveSelectedToGalleryButton = memo(() => {
   const canvasManager = useCanvasManager();
   const autoAddBoardId = useAppSelector(selectAutoAddBoardId);
-  const ctx = useCanvasSessionContext();
-  const selectedItemOutputImageDTO = useStore(ctx.$selectedItemOutputImageDTO);
+  const ctx = useStagingAreaContext();
+  const selectedItemImageDTO = useStore(ctx.$selectedItemImageDTO);
   const shouldShowStagedImage = useStore(canvasManager.stagingArea.$shouldShowStagedImage);
 
   const { t } = useTranslation();
 
   const saveSelectedImageToGallery = useCallback(async () => {
-    if (!selectedItemOutputImageDTO) {
+    if (!selectedItemImageDTO) {
       return;
     }
 
@@ -31,7 +31,7 @@ export const StagingAreaToolbarSaveSelectedToGalleryButton = memo(() => {
     // the gallery without borking the canvas, which may need this image to exist.
     const result = await withResultAsync(async () => {
       // Create a new file with the same name, which we will upload
-      await copyImage(selectedItemOutputImageDTO.image_name, {
+      await copyImage(selectedItemImageDTO.image_name, {
         // Image should show up in the Images tab
         image_category: 'general',
         is_intermediate: false,
@@ -55,7 +55,7 @@ export const StagingAreaToolbarSaveSelectedToGalleryButton = memo(() => {
         status: 'error',
       });
     }
-  }, [autoAddBoardId, selectedItemOutputImageDTO, t]);
+  }, [autoAddBoardId, selectedItemImageDTO, t]);
 
   return (
     <IconButton
@@ -64,7 +64,7 @@ export const StagingAreaToolbarSaveSelectedToGalleryButton = memo(() => {
       icon={<PiFloppyDiskBold />}
       onClick={saveSelectedImageToGallery}
       colorScheme="invokeBlue"
-      isDisabled={!selectedItemOutputImageDTO || !shouldShowStagedImage}
+      isDisabled={!selectedItemImageDTO || !shouldShowStagedImage}
     />
   );
 });

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/__mocks__/mockStagingAreaApp.ts

@@ -0,0 +1,172 @@
+import { merge } from 'es-toolkit';
+import type { StagingAreaAppApi } from 'features/controlLayers/components/StagingArea/state';
+import type { AutoSwitchMode } from 'features/controlLayers/store/canvasSettingsSlice';
+import type { ImageDTO, S } from 'services/api/types';
+import type { PartialDeep } from 'type-fest';
+import { vi } from 'vitest';
+
+export const createMockStagingAreaApp = (): StagingAreaAppApi & {
+  // Additional methods for testing
+  _triggerItemsChanged: (items: S['SessionQueueItem'][]) => void;
+  _triggerQueueItemStatusChanged: (data: S['QueueItemStatusChangedEvent']) => void;
+  _triggerInvocationProgress: (data: S['InvocationProgressEvent']) => void;
+  _setAutoSwitchMode: (mode: AutoSwitchMode) => void;
+  _setImageDTO: (imageName: string, imageDTO: ImageDTO | null) => void;
+} => {
+  const itemsChangedHandlers = new Set<(items: S['SessionQueueItem'][]) => void>();
+  const queueItemStatusChangedHandlers = new Set<(data: S['QueueItemStatusChangedEvent']) => void>();
+  const invocationProgressHandlers = new Set<(data: S['InvocationProgressEvent']) => void>();
+
+  let autoSwitchMode: AutoSwitchMode = 'switch_on_start';
+  const imageDTOs = new Map<string, ImageDTO | null>();
+
+  return {
+    onDiscard: vi.fn(),
+    onDiscardAll: vi.fn(),
+    onAccept: vi.fn(),
+    onSelect: vi.fn(),
+    onSelectPrev: vi.fn(),
+    onSelectNext: vi.fn(),
+    onSelectFirst: vi.fn(),
+    onSelectLast: vi.fn(),
+    getAutoSwitch: vi.fn(() => autoSwitchMode),
+    onAutoSwitchChange: vi.fn(),
+    getImageDTO: vi.fn((imageName: string) => {
+      return Promise.resolve(imageDTOs.get(imageName) || null);
+    }),
+    onItemsChanged: vi.fn((handler) => {
+      itemsChangedHandlers.add(handler);
+      return () => itemsChangedHandlers.delete(handler);
+    }),
+    onQueueItemStatusChanged: vi.fn((handler) => {
+      queueItemStatusChangedHandlers.add(handler);
+      return () => queueItemStatusChangedHandlers.delete(handler);
+    }),
+    onInvocationProgress: vi.fn((handler) => {
+      invocationProgressHandlers.add(handler);
+      return () => invocationProgressHandlers.delete(handler);
+    }),
+
+    // Testing helper methods
+    _triggerItemsChanged: (items: S['SessionQueueItem'][]) => {
+      itemsChangedHandlers.forEach((handler) => handler(items));
+    },
+    _triggerQueueItemStatusChanged: (data: S['QueueItemStatusChangedEvent']) => {
+      queueItemStatusChangedHandlers.forEach((handler) => handler(data));
+    },
+    _triggerInvocationProgress: (data: S['InvocationProgressEvent']) => {
+      invocationProgressHandlers.forEach((handler) => handler(data));
+    },
+    _setAutoSwitchMode: (mode: AutoSwitchMode) => {
+      autoSwitchMode = mode;
+    },
+    _setImageDTO: (imageName: string, imageDTO: ImageDTO | null) => {
+      imageDTOs.set(imageName, imageDTO);
+    },
+  };
+};
+
+export const createMockQueueItem = (overrides: PartialDeep<S['SessionQueueItem']> = {}): S['SessionQueueItem'] =>
+  merge(
+    {
+      item_id: 1,
+      batch_id: 'test-batch-id',
+      session_id: 'test-session',
+      queue_id: 'test-queue-id',
+      status: 'pending',
+      priority: 0,
+      origin: null,
+      destination: 'test-session',
+      error_type: null,
+      error_message: null,
+      error_traceback: null,
+      created_at: '2024-01-01T00:00:00Z',
+      updated_at: '2024-01-01T00:00:00Z',
+      started_at: null,
+      completed_at: null,
+      field_values: null,
+      retried_from_item_id: null,
+      is_api_validation_run: false,
+      published_workflow_id: null,
+      session: {
+        id: 'test-session',
+        graph: {},
+        execution_graph: {},
+        executed: [],
+        executed_history: [],
+        results: {
+          'test-node-id': {
+            image: {
+              image_name: 'test-image.png',
+            },
+          },
+        },
+        errors: {},
+        prepared_source_mapping: {},
+        source_prepared_mapping: {
+          canvas_output: ['test-node-id'],
+        },
+      },
+      workflow: null,
+    },
+    overrides
+  ) as S['SessionQueueItem'];
+
+export const createMockImageDTO = (overrides: Partial<ImageDTO> = {}): ImageDTO => ({
+  image_name: 'test-image.png',
+  image_url: 'http://test.com/test-image.png',
+  thumbnail_url: 'http://test.com/test-image-thumb.png',
+  image_origin: 'internal',
+  image_category: 'general',
+  width: 512,
+  height: 512,
+  created_at: '2024-01-01T00:00:00Z',
+  updated_at: '2024-01-01T00:00:00Z',
+  deleted_at: null,
+  is_intermediate: false,
+  starred: false,
+  has_workflow: false,
+  session_id: 'test-session',
+  node_id: 'test-node-id',
+  board_id: null,
+  ...overrides,
+});
+
+export const createMockProgressEvent = (
+  overrides: PartialDeep<S['InvocationProgressEvent']> = {}
+): S['InvocationProgressEvent'] =>
+  merge(
+    {
+      timestamp: Date.now(),
+      queue_id: 'test-queue-id',
+      item_id: 1,
+      batch_id: 'test-batch-id',
+      session_id: 'test-session',
+      origin: null,
+      destination: 'test-session',
+      invocation: {},
+      invocation_source_id: 'test-invocation-source-id',
+      message: 'Processing...',
+      percentage: 50,
+      image: null,
+    } as S['InvocationProgressEvent'],
+    overrides
+  );
+
+export const createMockQueueItemStatusChangedEvent = (
+  overrides: PartialDeep<S['QueueItemStatusChangedEvent']> = {}
+): S['QueueItemStatusChangedEvent'] =>
+  merge(
+    {
+      timestamp: Date.now(),
+      queue_id: 'test-queue-id',
+      item_id: 1,
+      batch_id: 'test-batch-id',
+      origin: null,
+      destination: 'test-session',
+      status: 'completed',
+      error_type: null,
+      error_message: null,
+    } as S['QueueItemStatusChangedEvent'],
+    overrides
+  );

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/context.tsx

@@ -0,0 +1,132 @@
+import { useStore } from '@nanostores/react';
+import { useAppStore } from 'app/store/storeHooks';
+import {
+  selectStagingAreaAutoSwitch,
+  settingsStagingAreaAutoSwitchChanged,
+} from 'features/controlLayers/store/canvasSettingsSlice';
+import { rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
+import {
+  buildSelectCanvasQueueItems,
+  canvasQueueItemDiscarded,
+  canvasSessionReset,
+} from 'features/controlLayers/store/canvasStagingAreaSlice';
+import { selectBboxRect, selectSelectedEntityIdentifier } from 'features/controlLayers/store/selectors';
+import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
+import { imageNameToImageObject } from 'features/controlLayers/store/util';
+import type { PropsWithChildren } from 'react';
+import { createContext, memo, useContext, useEffect, useMemo, useState } from 'react';
+import { getImageDTOSafe } from 'services/api/endpoints/images';
+import { queueApi } from 'services/api/endpoints/queue';
+import type { S } from 'services/api/types';
+import { $socket } from 'services/events/stores';
+import { assert } from 'tsafe';
+
+import type { ProgressData, StagingAreaAppApi } from './state';
+import { getInitialProgressData, StagingAreaApi } from './state';
+
+const StagingAreaContext = createContext<StagingAreaApi | null>(null);
+
+export const StagingAreaContextProvider = memo(({ children, sessionId }: PropsWithChildren<{ sessionId: string }>) => {
+  const store = useAppStore();
+  const socket = useStore($socket);
+  const stagingAreaAppApi = useMemo<StagingAreaAppApi>(() => {
+    const selectQueueItems = buildSelectCanvasQueueItems(sessionId);
+
+    const _stagingAreaAppApi: StagingAreaAppApi = {
+      getAutoSwitch: () => selectStagingAreaAutoSwitch(store.getState()),
+      getImageDTO: (imageName: string) => getImageDTOSafe(imageName),
+      onInvocationProgress: (handler) => {
+        socket?.on('invocation_progress', handler);
+        return () => {
+          socket?.off('invocation_progress', handler);
+        };
+      },
+      onQueueItemStatusChanged: (handler) => {
+        socket?.on('queue_item_status_changed', handler);
+        return () => {
+          socket?.off('queue_item_status_changed', handler);
+        };
+      },
+      onItemsChanged: (handler) => {
+        let prev: S['SessionQueueItem'][] = [];
+        return store.subscribe(() => {
+          const next = selectQueueItems(store.getState());
+          if (prev !== next) {
+            prev = next;
+            handler(next);
+          }
+        });
+      },
+      onDiscard: ({ item_id, status }) => {
+        store.dispatch(canvasQueueItemDiscarded({ itemId: item_id }));
+        if (status === 'in_progress' || status === 'pending') {
+          store.dispatch(queueApi.endpoints.cancelQueueItem.initiate({ item_id }, { track: false }));
+        }
+      },
+      onDiscardAll: () => {
+        store.dispatch(canvasSessionReset());
+        store.dispatch(
+          queueApi.endpoints.cancelQueueItemsByDestination.initiate({ destination: sessionId }, { track: false })
+        );
+      },
+      onAccept: (item, imageDTO) => {
+        const bboxRect = selectBboxRect(store.getState());
+        const { x, y, width, height } = bboxRect;
+        const imageObject = imageNameToImageObject(imageDTO.image_name, { width, height });
+        const selectedEntityIdentifier = selectSelectedEntityIdentifier(store.getState());
+        const overrides: Partial<CanvasRasterLayerState> = {
+          position: { x, y },
+          objects: [imageObject],
+        };
+
+        store.dispatch(rasterLayerAdded({ overrides, isSelected: selectedEntityIdentifier?.type === 'raster_layer' }));
+        store.dispatch(canvasSessionReset());
+        store.dispatch(
+          queueApi.endpoints.cancelQueueItemsByDestination.initiate({ destination: sessionId }, { track: false })
+        );
+      },
+      onAutoSwitchChange: (mode) => {
+        store.dispatch(settingsStagingAreaAutoSwitchChanged(mode));
+      },
+    };
+
+    return _stagingAreaAppApi;
+  }, [sessionId, socket, store]);
+
+  const [stagingAreaApi] = useState(() => new StagingAreaApi());
+
+  useEffect(() => {
+    stagingAreaApi.connectToApp(sessionId, stagingAreaAppApi);
+
+    // We need to subscribe to the queue items query manually to ensure the staging area actually gets the items
+    const { unsubscribe: unsubQueueItemsQuery } = store.dispatch(
+      queueApi.endpoints.listAllQueueItems.initiate({ destination: sessionId })
+    );
+
+    return () => {
+      stagingAreaApi.cleanup();
+      unsubQueueItemsQuery();
+    };
+  }, [sessionId, stagingAreaApi, stagingAreaAppApi, store]);
+
+  return <StagingAreaContext.Provider value={stagingAreaApi}>{children}</StagingAreaContext.Provider>;
+});
+StagingAreaContextProvider.displayName = 'StagingAreaContextProvider';
+
+export const useStagingAreaContext = () => {
+  const ctx = useContext(StagingAreaContext);
+  assert(ctx !== null, "'useStagingAreaContext' must be used within a StagingAreaContextProvider");
+  return ctx;
+};
+
+export const useOutputImageDTO = (itemId: number) => {
+  const ctx = useStagingAreaContext();
+  const allProgressData = useStore(ctx.$progressData, { keys: [itemId] });
+  return allProgressData[itemId]?.imageDTO ?? null;
+};
+
+export const useProgressDatum = (itemId: number): ProgressData => {
+  const ctx = useStagingAreaContext();
+  const allProgressData = useStore(ctx.$progressData, { keys: [itemId] });
+  return allProgressData[itemId] ?? getInitialProgressData(itemId);
+};

invokeai/frontend/web/src/features/controlLayers/components/StagingArea/shared.test.ts

@@ -0,0 +1,205 @@
+import type { S } from 'services/api/types';
+import { describe, expect, it } from 'vitest';
+
+import { getOutputImageName, getProgressMessage, getQueueItemElementId } from './shared';
+
+describe('StagingAreaApi Utility Functions', () => {
+  describe('getProgressMessage', () => {
+    it('should return default message when no data provided', () => {
+      expect(getProgressMessage()).toBe('Generating');
+      expect(getProgressMessage(null)).toBe('Generating');
+    });
+
+    it('should format progress message when data is provided', () => {
+      const progressEvent: S['InvocationProgressEvent'] = {
+        item_id: 1,
+        destination: 'test-session',
+        node_id: 'test-node',
+        source_node_id: 'test-source-node',
+        progress: 0.5,
+        message: 'Processing image...',
+        image: null,
+      } as unknown as S['InvocationProgressEvent'];
+
+      const result = getProgressMessage(progressEvent);
+      expect(result).toBe('Processing image...');
+    });
+  });
+
+  describe('getQueueItemElementId', () => {
+    it('should generate correct element ID for queue item', () => {
+      expect(getQueueItemElementId(0)).toBe('queue-item-preview-0');
+      expect(getQueueItemElementId(5)).toBe('queue-item-preview-5');
+      expect(getQueueItemElementId(99)).toBe('queue-item-preview-99');
+    });
+  });
+
+  describe('getOutputImageName', () => {
+    it('should extract image name from completed queue item', () => {
+      const queueItem: S['SessionQueueItem'] = {
+        item_id: 1,
+        status: 'completed',
+        priority: 0,
+        destination: 'test-session',
+        created_at: '2024-01-01T00:00:00Z',
+        updated_at: '2024-01-01T00:00:00Z',
+        started_at: '2024-01-01T00:00:01Z',
+        completed_at: '2024-01-01T00:01:00Z',
+        error: null,
+        session: {
+          id: 'test-session',
+          source_prepared_mapping: {
+            canvas_output: ['output-node-id'],
+          },
+          results: {
+            'output-node-id': {
+              image: {
+                image_name: 'test-output.png',
+              },
+            },
+          },
+        },
+      } as unknown as S['SessionQueueItem'];
+
+      expect(getOutputImageName(queueItem)).toBe('test-output.png');
+    });
+
+    it('should return null when no canvas output node found', () => {
+      const queueItem = {
+        item_id: 1,
+        status: 'completed',
+        priority: 0,
+        destination: 'test-session',
+        created_at: '2024-01-01T00:00:00Z',
+        updated_at: '2024-01-01T00:00:00Z',
+        started_at: '2024-01-01T00:00:01Z',
+        completed_at: '2024-01-01T00:01:00Z',
+        error: null,
+        session: {
+          id: 'test-session',
+          source_prepared_mapping: {
+            some_other_node: ['other-node-id'],
+          },
+          results: {
+            'other-node-id': {
+              type: 'image_output',
+              image: {
+                image_name: 'test-output.png',
+              },
+              width: 512,
+              height: 512,
+            },
+          },
+        },
+      } as unknown as S['SessionQueueItem'];
+
+      expect(getOutputImageName(queueItem)).toBe(null);
+    });
+
+    it('should return null when output node has no results', () => {
+      const queueItem: S['SessionQueueItem'] = {
+        item_id: 1,
+        status: 'completed',
+        priority: 0,
+        destination: 'test-session',
+        created_at: '2024-01-01T00:00:00Z',
+        updated_at: '2024-01-01T00:00:00Z',
+        started_at: '2024-01-01T00:00:01Z',
+        completed_at: '2024-01-01T00:01:00Z',
+        error: null,
+        session: {
+          id: 'test-session',
+          source_prepared_mapping: {
+            canvas_output: ['output-node-id'],
+          },
+          results: {},
+        },
+      } as unknown as S['SessionQueueItem'];
+
+      expect(getOutputImageName(queueItem)).toBe(null);
+    });
+
+    it('should return null when results contain no image fields', () => {
+      const queueItem: S['SessionQueueItem'] = {
+        item_id: 1,
+        status: 'completed',
+        priority: 0,
+        destination: 'test-session',
+        created_at: '2024-01-01T00:00:00Z',
+        updated_at: '2024-01-01T00:00:00Z',
+        started_at: '2024-01-01T00:00:01Z',
+        completed_at: '2024-01-01T00:01:00Z',
+        error: null,
+        session: {
+          id: 'test-session',
+          source_prepared_mapping: {
+            canvas_output: ['output-node-id'],
+          },
+          results: {
+            'output-node-id': {
+              text: 'some text output',
+              number: 42,
+            },
+          },
+        },
+      } as unknown as S['SessionQueueItem'];
+
+      expect(getOutputImageName(queueItem)).toBe(null);
+    });
+
+    it('should handle multiple outputs and return first image', () => {
+      const queueItem: S['SessionQueueItem'] = {
+        item_id: 1,
+        status: 'completed',
+        priority: 0,
+        destination: 'test-session',
+        created_at: '2024-01-01T00:00:00Z',
+        updated_at: '2024-01-01T00:00:00Z',
+        started_at: '2024-01-01T00:00:01Z',
+        completed_at: '2024-01-01T00:01:00Z',
+        error: null,
+        session: {
+          id: 'test-session',
+          source_prepared_mapping: {
+            canvas_output: ['output-node-id'],
+          },
+          results: {
+            'output-node-id': {
+              text: 'some text',
+              first_image: {
+                image_name: 'first-image.png',
+              },
+              second_image: {
+                image_name: 'second-image.png',
+              },
+            },
+          },
+        },
+      } as unknown as S['SessionQueueItem'];
+
+      const result = getOutputImageName(queueItem);
+      expect(result).toBe('first-image.png');
+    });
+
+    it('should handle empty session mapping', () => {
+      const queueItem: S['SessionQueueItem'] = {
+        item_id: 1,
+        status: 'completed',
+        priority: 0,
+        destination: 'test-session',
+        created_at: '2024-01-01T00:00:00Z',
+        updated_at: '2024-01-01T00:00:00Z',
+        started_at: '2024-01-01T00:00:01Z',
+        completed_at: '2024-01-01T00:01:00Z',
+        error: null,
+        session: {
+          id: 'test-session',
+          source_prepared_mapping: {},
+          results: {},
+        },
+      } as unknown as S['SessionQueueItem'];
+
+      expect(getOutputImageName(queueItem)).toBe(null);
+    });
+  });
+});