{release} 3.6.3rc1

docker/Dockerfile

@@ -18,8 +18,8 @@ ENV INVOKEAI_SRC=/opt/invokeai
 ENV VIRTUAL_ENV=/opt/venv/invokeai
 
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.1.2
-ARG TORCHVISION_VERSION=0.16.2
+ARG TORCH_VERSION=2.1.0
+ARG TORCHVISION_VERSION=0.16
 ARG GPU_DRIVER=cuda
 ARG TARGETPLATFORM="linux/amd64"
 # unused but available
@@ -35,7 +35,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
     elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.6"; \
+        extra_index_url_arg="--index-url https://download.pytorch.org/whl/rocm5.6"; \
     else \
         extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
     fi &&\
@@ -54,7 +54,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
         pip install -e ".[xformers]"; \
     else \
-        pip install $extra_index_url_arg -e "."; \
+        pip install -e "."; \
     fi
 
 # #### Build the Web UI ------------------------------------

docker/README.md

@@ -28,7 +28,7 @@ This is done via Docker Desktop preferences
 
 ### Configure Invoke environment
 
-1. Make a copy of `.env.sample` and name it `.env` (`cp .env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
+1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
     a. the desired location of the InvokeAI runtime directory, or
     b. an existing, v3.0.0 compatible runtime directory.
 1. Execute `run.sh`

docker/run.sh

@@ -21,7 +21,7 @@ run() {
     printf "%s\n" "$build_args"
   fi
 
-  docker compose build $build_args $service_name
+  docker compose build $build_args
   unset build_args
 
   printf "%s\n" "starting service $service_name"

docs/features/CONTROLNET.md

@@ -94,8 +94,6 @@ A model that helps generate creative QR codes that still scan. Can also be used
 **Openpose**: 
 The OpenPose control model allows for the identification of the general pose of a character by pre-processing an existing image with a clear human structure. With advanced options, Openpose can also detect the face or hands in the image. 
 
-*Note:* The DWPose Processor has replaced the OpenPose processor in Invoke. Workflows and generations that relied on the OpenPose Processor will need to be updated to use the DWPose Processor instead.
-
 **Mediapipe Face**:
 
 The MediaPipe Face identification processor is able to clearly identify facial features in order to capture vivid expressions of human faces.

docs/installation/020_INSTALL_MANUAL.md

@@ -230,13 +230,13 @@ manager, please follow these steps:
         === "local Webserver"
 
             ```bash
-            invokeai-web
+            invokeai --web
             ```
 
         === "Public Webserver"
 
             ```bash
-            invokeai-web --host 0.0.0.0
+            invokeai --web --host 0.0.0.0
             ```
 
         === "CLI"
@@ -402,4 +402,4 @@ environment variable INVOKEAI_ROOT to point to the installation directory.
 Note that if you run into problems with the Conda installation, the InvokeAI
 staff will **not** be able to help you out. Caveat Emptor!
 
-[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939
+[dev-chat]: https://discord.com/channels/1020123559063990373/1049495067846524939

docs/installation/040_INSTALL_DOCKER.md

@@ -69,7 +69,7 @@ a token and copy it, since you will need in for the next step.
 
 ### Setup
 
-Set up your environmnent variables. In the `docker` directory, make a copy of `.env.sample` and name it `.env`. Make changes as necessary.
+Set up your environmnent variables. In the `docker` directory, make a copy of `env.sample` and name it `.env`. Make changes as necessary.
 
 Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
 

docs/nodes/defaultNodes.md

@@ -81,7 +81,7 @@ their descriptions.
 | ONNX Text to Latents                                          | Generates latents from conditionings.                                                                                                                |
 | ONNX Model Loader                                             | Loads a main model, outputting its submodels.                                                                                                        |
 | OpenCV Inpaint                                                | Simple inpaint using opencv.                                                                                                                         |
-| DW Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
+| Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
 | PIDI Processor                                                | Applies PIDI processing to image                                                                                                                     |
 | Prompts from File                                             | Loads prompts from a text file                                                                                                                       |
 | Random Integer                                                | Outputs a single random integer.                                                                                                                     |

installer/lib/messages.py

@@ -91,8 +91,8 @@ def choose_version(available_releases: tuple | None = None) -> str:
         complete_while_typing=True,
         completer=FuzzyWordCompleter(choices),
     )
-    console.print(f"   Version {choices[0] if response == '' else response} will be installed.")
 
+    console.print(f"   Version {choices[0]} will be installed.")
     console.line()
 
     return "stable" if response == "" else response

invokeai/app/api/sockets.py

@@ -14,7 +14,7 @@ class SocketIO:
 
     def __init__(self, app: FastAPI):
         self.__sio = AsyncServer(async_mode="asgi", cors_allowed_origins="*")
-        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="/ws/socket.io")
+        self.__app = ASGIApp(socketio_server=self.__sio, socketio_path="socket.io")
         app.mount("/ws", self.__app)
 
         self.__sio.on("subscribe_queue", handler=self._handle_sub_queue)

invokeai/app/invocations/controlnet_image_processors.py

@@ -17,6 +17,7 @@ from controlnet_aux import (
     MidasDetector,
     MLSDdetector,
     NormalBaeDetector,
+    OpenposeDetector,
     PidiNetDetector,
     SamDetector,
     ZoeDetector,
@@ -30,7 +31,6 @@ from invokeai.app.invocations.util import validate_begin_end_step, validate_weig
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
-from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
 
 from ...backend.model_management import BaseModelType
 from .baseinvocation import (
@@ -276,6 +276,31 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
         return processed_image
 
 
+@invocation(
+    "openpose_image_processor",
+    title="Openpose Processor",
+    tags=["controlnet", "openpose", "pose"],
+    category="controlnet",
+    version="1.2.0",
+)
+class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies Openpose processing to image"""
+
+    hand_and_face: bool = InputField(default=False, description="Whether to use hands and face mode")
+    detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
+
+    def run_processor(self, image):
+        openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = openpose_processor(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            hand_and_face=self.hand_and_face,
+        )
+        return processed_image
+
+
 @invocation(
     "midas_depth_image_processor",
     title="Midas Depth Processor",
@@ -599,7 +624,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
     offload: bool = InputField(default=False)
 
-    def run_processor(self, image: Image.Image):
+    def run_processor(self, image):
         depth_anything_detector = DepthAnythingDetector()
         depth_anything_detector.load_model(model_size=self.model_size)
 
@@ -608,30 +633,3 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
 
         processed_image = depth_anything_detector(image=image, resolution=self.resolution, offload=self.offload)
         return processed_image
-
-
-@invocation(
-    "dw_openpose_image_processor",
-    title="DW Openpose Image Processor",
-    tags=["controlnet", "dwpose", "openpose"],
-    category="controlnet",
-    version="1.0.0",
-)
-class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
-    """Generates an openpose pose from an image using DWPose"""
-
-    draw_body: bool = InputField(default=True)
-    draw_face: bool = InputField(default=False)
-    draw_hands: bool = InputField(default=False)
-    image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
-
-    def run_processor(self, image):
-        dw_openpose = DWOpenposeDetector()
-        processed_image = dw_openpose(
-            image,
-            draw_face=self.draw_face,
-            draw_hands=self.draw_hands,
-            draw_body=self.draw_body,
-            resolution=self.image_resolution,
-        )
-        return processed_image

invokeai/app/invocations/upscale.py

@@ -5,12 +5,12 @@ from typing import Literal
 import cv2
 import numpy as np
 import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
 from PIL import Image
 from pydantic import ConfigDict
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
 from invokeai.backend.util.devices import choose_torch_device
 

invokeai/app/services/images/images_default.py

@@ -154,7 +154,7 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Image record not found")
             raise
         except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image metadata")
+            self.__invoker.services.logger.error("Problem getting image DTO")
             raise e
 
     def get_workflow(self, image_name: str) -> Optional[WorkflowWithoutID]:

invokeai/app/services/shared/graph.py

@@ -2,7 +2,7 @@
 
 import copy
 import itertools
-from typing import Annotated, Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
+from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
 
 import networkx as nx
 from pydantic import BaseModel, ConfigDict, field_validator, model_validator
@@ -141,16 +141,6 @@ def are_connections_compatible(
     return are_connection_types_compatible(from_node_field, to_node_field)
 
 
-T = TypeVar("T")
-
-
-def copydeep(obj: T) -> T:
-    """Deep-copies an object. If it is a pydantic model, use the model's copy method."""
-    if isinstance(obj, BaseModel):
-        return obj.model_copy(deep=True)
-    return copy.deepcopy(obj)
-
-
 class NodeAlreadyInGraphError(ValueError):
     pass
 
@@ -540,7 +530,7 @@ class Graph(BaseModel):
         except NodeNotFoundError:
             return False
 
-    def get_node(self, node_path: str) -> BaseInvocation:
+    def get_node(self, node_path: str) -> InvocationsUnion:
         """Gets a node from the graph using a node path."""
         # Materialized graphs may have nodes at the top level
         graph, node_id = self._get_graph_and_node(node_path)
@@ -891,7 +881,7 @@ class GraphExecutionState(BaseModel):
         # If next is still none, there's no next node, return None
         return next_node
 
-    def complete(self, node_id: str, output: BaseInvocationOutput) -> None:
+    def complete(self, node_id: str, output: InvocationOutputsUnion):
         """Marks a node as complete"""
 
         if node_id not in self.execution_graph.nodes:
@@ -1128,22 +1118,17 @@ class GraphExecutionState(BaseModel):
 
     def _prepare_inputs(self, node: BaseInvocation):
         input_edges = [e for e in self.execution_graph.edges if e.destination.node_id == node.id]
-        # Inputs must be deep-copied, else if a node mutates the object, other nodes that get the same input
-        # will see the mutation.
         if isinstance(node, CollectInvocation):
             output_collection = [
-                copydeep(getattr(self.results[edge.source.node_id], edge.source.field))
+                getattr(self.results[edge.source.node_id], edge.source.field)
                 for edge in input_edges
                 if edge.destination.field == "item"
             ]
             node.collection = output_collection
         else:
             for edge in input_edges:
-                setattr(
-                    node,
-                    edge.destination.field,
-                    copydeep(getattr(self.results[edge.source.node_id], edge.source.field)),
-                )
+                output_value = getattr(self.results[edge.source.node_id], edge.source.field)
+                setattr(node, edge.destination.field, output_value)
 
     # TODO: Add API for modifying underlying graph that checks if the change will be valid given the current execution state
     def _is_edge_valid(self, edge: Edge) -> bool:

invokeai/backend/image_util/basicsr/LICENSE

@@ -1,201 +0,0 @@
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-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-      and distribution as defined by Sections 1 through 9 of this document.
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-
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-      "You" (or "Your") shall mean an individual or Legal Entity
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-      To apply the Apache License to your work, attach the following
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invokeai/backend/image_util/basicsr/__init__.py

@@ -1,18 +0,0 @@
-"""
-Adapted from https://github.com/XPixelGroup/BasicSR
-License: Apache-2.0
-
-As of Feb 2024, `basicsr` appears to be unmaintained. It imports a function from `torchvision` that is removed in
-`torchvision` 0.17. Here is the deprecation warning:
-
-    UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in
-    0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in
-    torchvision.transforms.v2.functional.
-
-As a result, a dependency on `basicsr` means we cannot keep our `torchvision` dependency up to date.
-
-Because we only rely on a single class `RRDBNet` from `basicsr`, we've copied the relevant code here and removed the
-dependency on `basicsr`.
-
-The code is almost unchanged, only a few type annotations have been added. The license is also copied.
-"""

invokeai/backend/image_util/basicsr/arch_util.py

@@ -1,75 +0,0 @@
-from typing import Type
-
-import torch
-from torch import nn as nn
-from torch.nn import init as init
-from torch.nn.modules.batchnorm import _BatchNorm
-
-
-@torch.no_grad()
-def default_init_weights(
-    module_list: list[nn.Module] | nn.Module, scale: float = 1, bias_fill: float = 0, **kwargs
-) -> None:
-    """Initialize network weights.
-
-    Args:
-        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
-        scale (float): Scale initialized weights, especially for residual
-            blocks. Default: 1.
-        bias_fill (float): The value to fill bias. Default: 0
-        kwargs (dict): Other arguments for initialization function.
-    """
-    if not isinstance(module_list, list):
-        module_list = [module_list]
-    for module in module_list:
-        for m in module.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, _BatchNorm):
-                init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-
-
-def make_layer(basic_block: Type[nn.Module], num_basic_block: int, **kwarg) -> nn.Sequential:
-    """Make layers by stacking the same blocks.
-
-    Args:
-        basic_block (Type[nn.Module]): nn.Module class for basic block.
-        num_basic_block (int): number of blocks.
-
-    Returns:
-        nn.Sequential: Stacked blocks in nn.Sequential.
-    """
-    layers = []
-    for _ in range(num_basic_block):
-        layers.append(basic_block(**kwarg))
-    return nn.Sequential(*layers)
-
-
-# TODO: may write a cpp file
-def pixel_unshuffle(x: torch.Tensor, scale: int) -> torch.Tensor:
-    """Pixel unshuffle.
-
-    Args:
-        x (Tensor): Input feature with shape (b, c, hh, hw).
-        scale (int): Downsample ratio.
-
-    Returns:
-        Tensor: the pixel unshuffled feature.
-    """
-    b, c, hh, hw = x.size()
-    out_channel = c * (scale**2)
-    assert hh % scale == 0 and hw % scale == 0
-    h = hh // scale
-    w = hw // scale
-    x_view = x.view(b, c, h, scale, w, scale)
-    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)

invokeai/backend/image_util/basicsr/rrdbnet_arch.py

@@ -1,125 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-
-from .arch_util import default_init_weights, make_layer, pixel_unshuffle
-
-
-class ResidualDenseBlock(nn.Module):
-    """Residual Dense Block.
-
-    Used in RRDB block in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat: int = 64, num_grow_ch: int = 32) -> None:
-        super(ResidualDenseBlock, self).__init__()
-        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
-        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
-        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-        # initialization
-        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x1 = self.lrelu(self.conv1(x))
-        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        # Empirically, we use 0.2 to scale the residual for better performance
-        return x5 * 0.2 + x
-
-
-class RRDB(nn.Module):
-    """Residual in Residual Dense Block.
-
-    Used in RRDB-Net in ESRGAN.
-
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        num_grow_ch (int): Channels for each growth.
-    """
-
-    def __init__(self, num_feat: int, num_grow_ch: int = 32) -> None:
-        super(RRDB, self).__init__()
-        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
-        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = self.rdb1(x)
-        out = self.rdb2(out)
-        out = self.rdb3(out)
-        # Empirically, we use 0.2 to scale the residual for better performance
-        return out * 0.2 + x
-
-
-class RRDBNet(nn.Module):
-    """Networks consisting of Residual in Residual Dense Block, which is used
-    in ESRGAN.
-
-    ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
-
-    We extend ESRGAN for scale x2 and scale x1.
-    Note: This is one option for scale 1, scale 2 in RRDBNet.
-    We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size
-    and enlarge the channel size before feeding inputs into the main ESRGAN architecture.
-
-    Args:
-        num_in_ch (int): Channel number of inputs.
-        num_out_ch (int): Channel number of outputs.
-        num_feat (int): Channel number of intermediate features.
-            Default: 64
-        num_block (int): Block number in the trunk network. Defaults: 23
-        num_grow_ch (int): Channels for each growth. Default: 32.
-    """
-
-    def __init__(
-        self,
-        num_in_ch: int,
-        num_out_ch: int,
-        scale: int = 4,
-        num_feat: int = 64,
-        num_block: int = 23,
-        num_grow_ch: int = 32,
-    ) -> None:
-        super(RRDBNet, self).__init__()
-        self.scale = scale
-        if scale == 2:
-            num_in_ch = num_in_ch * 4
-        elif scale == 1:
-            num_in_ch = num_in_ch * 16
-        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
-        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        # upsample
-        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if self.scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        elif self.scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        else:
-            feat = x
-        feat = self.conv_first(feat)
-        body_feat = self.conv_body(self.body(feat))
-        feat = feat + body_feat
-        # upsample
-        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode="nearest")))
-        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode="nearest")))
-        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        return out

invokeai/backend/image_util/dw_openpose/__init__.py

@@ -1,81 +0,0 @@
-import numpy as np
-import torch
-from controlnet_aux.util import resize_image
-from PIL import Image
-
-from invokeai.backend.image_util.dw_openpose.utils import draw_bodypose, draw_facepose, draw_handpose
-from invokeai.backend.image_util.dw_openpose.wholebody import Wholebody
-
-
-def draw_pose(pose, H, W, draw_face=True, draw_body=True, draw_hands=True, resolution=512):
-    bodies = pose["bodies"]
-    faces = pose["faces"]
-    hands = pose["hands"]
-    candidate = bodies["candidate"]
-    subset = bodies["subset"]
-    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
-
-    if draw_body:
-        canvas = draw_bodypose(canvas, candidate, subset)
-
-    if draw_hands:
-        canvas = draw_handpose(canvas, hands)
-
-    if draw_face:
-        canvas = draw_facepose(canvas, faces)
-
-    dwpose_image = resize_image(
-        canvas,
-        resolution,
-    )
-    dwpose_image = Image.fromarray(dwpose_image)
-
-    return dwpose_image
-
-
-class DWOpenposeDetector:
-    """
-    Code from the original implementation of the DW Openpose Detector.
-    Credits: https://github.com/IDEA-Research/DWPose
-    """
-
-    def __init__(self) -> None:
-        self.pose_estimation = Wholebody()
-
-    def __call__(
-        self, image: Image.Image, draw_face=False, draw_body=True, draw_hands=False, resolution=512
-    ) -> Image.Image:
-        np_image = np.array(image)
-        H, W, C = np_image.shape
-
-        with torch.no_grad():
-            candidate, subset = self.pose_estimation(np_image)
-            nums, keys, locs = candidate.shape
-            candidate[..., 0] /= float(W)
-            candidate[..., 1] /= float(H)
-            body = candidate[:, :18].copy()
-            body = body.reshape(nums * 18, locs)
-            score = subset[:, :18]
-            for i in range(len(score)):
-                for j in range(len(score[i])):
-                    if score[i][j] > 0.3:
-                        score[i][j] = int(18 * i + j)
-                    else:
-                        score[i][j] = -1
-
-            un_visible = subset < 0.3
-            candidate[un_visible] = -1
-
-            # foot = candidate[:, 18:24]
-
-            faces = candidate[:, 24:92]
-
-            hands = candidate[:, 92:113]
-            hands = np.vstack([hands, candidate[:, 113:]])
-
-            bodies = {"candidate": body, "subset": score}
-            pose = {"bodies": bodies, "hands": hands, "faces": faces}
-
-            return draw_pose(
-                pose, H, W, draw_face=draw_face, draw_hands=draw_hands, draw_body=draw_body, resolution=resolution
-            )

invokeai/backend/image_util/dw_openpose/onnxdet.py

@@ -1,128 +0,0 @@
-# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
-
-import cv2
-import numpy as np
-
-
-def nms(boxes, scores, nms_thr):
-    """Single class NMS implemented in Numpy."""
-    x1 = boxes[:, 0]
-    y1 = boxes[:, 1]
-    x2 = boxes[:, 2]
-    y2 = boxes[:, 3]
-
-    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-    order = scores.argsort()[::-1]
-
-    keep = []
-    while order.size > 0:
-        i = order[0]
-        keep.append(i)
-        xx1 = np.maximum(x1[i], x1[order[1:]])
-        yy1 = np.maximum(y1[i], y1[order[1:]])
-        xx2 = np.minimum(x2[i], x2[order[1:]])
-        yy2 = np.minimum(y2[i], y2[order[1:]])
-
-        w = np.maximum(0.0, xx2 - xx1 + 1)
-        h = np.maximum(0.0, yy2 - yy1 + 1)
-        inter = w * h
-        ovr = inter / (areas[i] + areas[order[1:]] - inter)
-
-        inds = np.where(ovr <= nms_thr)[0]
-        order = order[inds + 1]
-
-    return keep
-
-
-def multiclass_nms(boxes, scores, nms_thr, score_thr):
-    """Multiclass NMS implemented in Numpy. Class-aware version."""
-    final_dets = []
-    num_classes = scores.shape[1]
-    for cls_ind in range(num_classes):
-        cls_scores = scores[:, cls_ind]
-        valid_score_mask = cls_scores > score_thr
-        if valid_score_mask.sum() == 0:
-            continue
-        else:
-            valid_scores = cls_scores[valid_score_mask]
-            valid_boxes = boxes[valid_score_mask]
-            keep = nms(valid_boxes, valid_scores, nms_thr)
-            if len(keep) > 0:
-                cls_inds = np.ones((len(keep), 1)) * cls_ind
-                dets = np.concatenate([valid_boxes[keep], valid_scores[keep, None], cls_inds], 1)
-                final_dets.append(dets)
-    if len(final_dets) == 0:
-        return None
-    return np.concatenate(final_dets, 0)
-
-
-def demo_postprocess(outputs, img_size, p6=False):
-    grids = []
-    expanded_strides = []
-    strides = [8, 16, 32] if not p6 else [8, 16, 32, 64]
-
-    hsizes = [img_size[0] // stride for stride in strides]
-    wsizes = [img_size[1] // stride for stride in strides]
-
-    for hsize, wsize, stride in zip(hsizes, wsizes, strides, strict=False):
-        xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
-        grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
-        grids.append(grid)
-        shape = grid.shape[:2]
-        expanded_strides.append(np.full((*shape, 1), stride))
-
-    grids = np.concatenate(grids, 1)
-    expanded_strides = np.concatenate(expanded_strides, 1)
-    outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
-    outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
-
-    return outputs
-
-
-def preprocess(img, input_size, swap=(2, 0, 1)):
-    if len(img.shape) == 3:
-        padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
-    else:
-        padded_img = np.ones(input_size, dtype=np.uint8) * 114
-
-    r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
-    resized_img = cv2.resize(
-        img,
-        (int(img.shape[1] * r), int(img.shape[0] * r)),
-        interpolation=cv2.INTER_LINEAR,
-    ).astype(np.uint8)
-    padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
-
-    padded_img = padded_img.transpose(swap)
-    padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
-    return padded_img, r
-
-
-def inference_detector(session, oriImg):
-    input_shape = (640, 640)
-    img, ratio = preprocess(oriImg, input_shape)
-
-    ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
-    output = session.run(None, ort_inputs)
-    predictions = demo_postprocess(output[0], input_shape)[0]
-
-    boxes = predictions[:, :4]
-    scores = predictions[:, 4:5] * predictions[:, 5:]
-
-    boxes_xyxy = np.ones_like(boxes)
-    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
-    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
-    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
-    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
-    boxes_xyxy /= ratio
-    dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
-    if dets is not None:
-        final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
-        isscore = final_scores > 0.3
-        iscat = final_cls_inds == 0
-        isbbox = [i and j for (i, j) in zip(isscore, iscat, strict=False)]
-        final_boxes = final_boxes[isbbox]
-    else:
-        final_boxes = np.array([])
-
-    return final_boxes

invokeai/backend/image_util/dw_openpose/onnxpose.py

@@ -1,361 +0,0 @@
-# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
-
-from typing import List, Tuple
-
-import cv2
-import numpy as np
-import onnxruntime as ort
-
-
-def preprocess(
-    img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256)
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
-    """Do preprocessing for RTMPose model inference.
-
-    Args:
-        img (np.ndarray): Input image in shape.
-        input_size (tuple): Input image size in shape (w, h).
-
-    Returns:
-        tuple:
-        - resized_img (np.ndarray): Preprocessed image.
-        - center (np.ndarray): Center of image.
-        - scale (np.ndarray): Scale of image.
-    """
-    # get shape of image
-    img_shape = img.shape[:2]
-    out_img, out_center, out_scale = [], [], []
-    if len(out_bbox) == 0:
-        out_bbox = [[0, 0, img_shape[1], img_shape[0]]]
-    for i in range(len(out_bbox)):
-        x0 = out_bbox[i][0]
-        y0 = out_bbox[i][1]
-        x1 = out_bbox[i][2]
-        y1 = out_bbox[i][3]
-        bbox = np.array([x0, y0, x1, y1])
-
-        # get center and scale
-        center, scale = bbox_xyxy2cs(bbox, padding=1.25)
-
-        # do affine transformation
-        resized_img, scale = top_down_affine(input_size, scale, center, img)
-
-        # normalize image
-        mean = np.array([123.675, 116.28, 103.53])
-        std = np.array([58.395, 57.12, 57.375])
-        resized_img = (resized_img - mean) / std
-
-        out_img.append(resized_img)
-        out_center.append(center)
-        out_scale.append(scale)
-
-    return out_img, out_center, out_scale
-
-
-def inference(sess: ort.InferenceSession, img: np.ndarray) -> np.ndarray:
-    """Inference RTMPose model.
-
-    Args:
-        sess (ort.InferenceSession): ONNXRuntime session.
-        img (np.ndarray): Input image in shape.
-
-    Returns:
-        outputs (np.ndarray): Output of RTMPose model.
-    """
-    all_out = []
-    # build input
-    for i in range(len(img)):
-        input = [img[i].transpose(2, 0, 1)]
-
-        # build output
-        sess_input = {sess.get_inputs()[0].name: input}
-        sess_output = []
-        for out in sess.get_outputs():
-            sess_output.append(out.name)
-
-        # run model
-        outputs = sess.run(sess_output, sess_input)
-        all_out.append(outputs)
-
-    return all_out
-
-
-def postprocess(
-    outputs: List[np.ndarray],
-    model_input_size: Tuple[int, int],
-    center: Tuple[int, int],
-    scale: Tuple[int, int],
-    simcc_split_ratio: float = 2.0,
-) -> Tuple[np.ndarray, np.ndarray]:
-    """Postprocess for RTMPose model output.
-
-    Args:
-        outputs (np.ndarray): Output of RTMPose model.
-        model_input_size (tuple): RTMPose model Input image size.
-        center (tuple): Center of bbox in shape (x, y).
-        scale (tuple): Scale of bbox in shape (w, h).
-        simcc_split_ratio (float): Split ratio of simcc.
-
-    Returns:
-        tuple:
-        - keypoints (np.ndarray): Rescaled keypoints.
-        - scores (np.ndarray): Model predict scores.
-    """
-    all_key = []
-    all_score = []
-    for i in range(len(outputs)):
-        # use simcc to decode
-        simcc_x, simcc_y = outputs[i]
-        keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio)
-
-        # rescale keypoints
-        keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2
-        all_key.append(keypoints[0])
-        all_score.append(scores[0])
-
-    return np.array(all_key), np.array(all_score)
-
-
-def bbox_xyxy2cs(bbox: np.ndarray, padding: float = 1.0) -> Tuple[np.ndarray, np.ndarray]:
-    """Transform the bbox format from (x,y,w,h) into (center, scale)
-
-    Args:
-        bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted
-            as (left, top, right, bottom)
-        padding (float): BBox padding factor that will be multilied to scale.
-            Default: 1.0
-
-    Returns:
-        tuple: A tuple containing center and scale.
-        - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or
-            (n, 2)
-        - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or
-            (n, 2)
-    """
-    # convert single bbox from (4, ) to (1, 4)
-    dim = bbox.ndim
-    if dim == 1:
-        bbox = bbox[None, :]
-
-    # get bbox center and scale
-    x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3])
-    center = np.hstack([x1 + x2, y1 + y2]) * 0.5
-    scale = np.hstack([x2 - x1, y2 - y1]) * padding
-
-    if dim == 1:
-        center = center[0]
-        scale = scale[0]
-
-    return center, scale
-
-
-def _fix_aspect_ratio(bbox_scale: np.ndarray, aspect_ratio: float) -> np.ndarray:
-    """Extend the scale to match the given aspect ratio.
-
-    Args:
-        scale (np.ndarray): The image scale (w, h) in shape (2, )
-        aspect_ratio (float): The ratio of ``w/h``
-
-    Returns:
-        np.ndarray: The reshaped image scale in (2, )
-    """
-    w, h = np.hsplit(bbox_scale, [1])
-    bbox_scale = np.where(w > h * aspect_ratio, np.hstack([w, w / aspect_ratio]), np.hstack([h * aspect_ratio, h]))
-    return bbox_scale
-
-
-def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray:
-    """Rotate a point by an angle.
-
-    Args:
-        pt (np.ndarray): 2D point coordinates (x, y) in shape (2, )
-        angle_rad (float): rotation angle in radian
-
-    Returns:
-        np.ndarray: Rotated point in shape (2, )
-    """
-    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
-    rot_mat = np.array([[cs, -sn], [sn, cs]])
-    return rot_mat @ pt
-
-
-def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray:
-    """To calculate the affine matrix, three pairs of points are required. This
-    function is used to get the 3rd point, given 2D points a & b.
-
-    The 3rd point is defined by rotating vector `a - b` by 90 degrees
-    anticlockwise, using b as the rotation center.
-
-    Args:
-        a (np.ndarray): The 1st point (x,y) in shape (2, )
-        b (np.ndarray): The 2nd point (x,y) in shape (2, )
-
-    Returns:
-        np.ndarray: The 3rd point.
-    """
-    direction = a - b
-    c = b + np.r_[-direction[1], direction[0]]
-    return c
-
-
-def get_warp_matrix(
-    center: np.ndarray,
-    scale: np.ndarray,
-    rot: float,
-    output_size: Tuple[int, int],
-    shift: Tuple[float, float] = (0.0, 0.0),
-    inv: bool = False,
-) -> np.ndarray:
-    """Calculate the affine transformation matrix that can warp the bbox area
-    in the input image to the output size.
-
-    Args:
-        center (np.ndarray[2, ]): Center of the bounding box (x, y).
-        scale (np.ndarray[2, ]): Scale of the bounding box
-            wrt [width, height].
-        rot (float): Rotation angle (degree).
-        output_size (np.ndarray[2, ] | list(2,)): Size of the
-            destination heatmaps.
-        shift (0-100%): Shift translation ratio wrt the width/height.
-            Default (0., 0.).
-        inv (bool): Option to inverse the affine transform direction.
-            (inv=False: src->dst or inv=True: dst->src)
-
-    Returns:
-        np.ndarray: A 2x3 transformation matrix
-    """
-    shift = np.array(shift)
-    src_w = scale[0]
-    dst_w = output_size[0]
-    dst_h = output_size[1]
-
-    # compute transformation matrix
-    rot_rad = np.deg2rad(rot)
-    src_dir = _rotate_point(np.array([0.0, src_w * -0.5]), rot_rad)
-    dst_dir = np.array([0.0, dst_w * -0.5])
-
-    # get four corners of the src rectangle in the original image
-    src = np.zeros((3, 2), dtype=np.float32)
-    src[0, :] = center + scale * shift
-    src[1, :] = center + src_dir + scale * shift
-    src[2, :] = _get_3rd_point(src[0, :], src[1, :])
-
-    # get four corners of the dst rectangle in the input image
-    dst = np.zeros((3, 2), dtype=np.float32)
-    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
-    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
-    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
-
-    if inv:
-        warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src))
-    else:
-        warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst))
-
-    return warp_mat
-
-
-def top_down_affine(
-    input_size: dict, bbox_scale: dict, bbox_center: dict, img: np.ndarray
-) -> Tuple[np.ndarray, np.ndarray]:
-    """Get the bbox image as the model input by affine transform.
-
-    Args:
-        input_size (dict): The input size of the model.
-        bbox_scale (dict): The bbox scale of the img.
-        bbox_center (dict): The bbox center of the img.
-        img (np.ndarray): The original image.
-
-    Returns:
-        tuple: A tuple containing center and scale.
-        - np.ndarray[float32]: img after affine transform.
-        - np.ndarray[float32]: bbox scale after affine transform.
-    """
-    w, h = input_size
-    warp_size = (int(w), int(h))
-
-    # reshape bbox to fixed aspect ratio
-    bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h)
-
-    # get the affine matrix
-    center = bbox_center
-    scale = bbox_scale
-    rot = 0
-    warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h))
-
-    # do affine transform
-    img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR)
-
-    return img, bbox_scale
-
-
-def get_simcc_maximum(simcc_x: np.ndarray, simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
-    """Get maximum response location and value from simcc representations.
-
-    Note:
-        instance number: N
-        num_keypoints: K
-        heatmap height: H
-        heatmap width: W
-
-    Args:
-        simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx)
-        simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy)
-
-    Returns:
-        tuple:
-        - locs (np.ndarray): locations of maximum heatmap responses in shape
-            (K, 2) or (N, K, 2)
-        - vals (np.ndarray): values of maximum heatmap responses in shape
-            (K,) or (N, K)
-    """
-    N, K, Wx = simcc_x.shape
-    simcc_x = simcc_x.reshape(N * K, -1)
-    simcc_y = simcc_y.reshape(N * K, -1)
-
-    # get maximum value locations
-    x_locs = np.argmax(simcc_x, axis=1)
-    y_locs = np.argmax(simcc_y, axis=1)
-    locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32)
-    max_val_x = np.amax(simcc_x, axis=1)
-    max_val_y = np.amax(simcc_y, axis=1)
-
-    # get maximum value across x and y axis
-    mask = max_val_x > max_val_y
-    max_val_x[mask] = max_val_y[mask]
-    vals = max_val_x
-    locs[vals <= 0.0] = -1
-
-    # reshape
-    locs = locs.reshape(N, K, 2)
-    vals = vals.reshape(N, K)
-
-    return locs, vals
-
-
-def decode(simcc_x: np.ndarray, simcc_y: np.ndarray, simcc_split_ratio) -> Tuple[np.ndarray, np.ndarray]:
-    """Modulate simcc distribution with Gaussian.
-
-    Args:
-        simcc_x (np.ndarray[K, Wx]): model predicted simcc in x.
-        simcc_y (np.ndarray[K, Wy]): model predicted simcc in y.
-        simcc_split_ratio (int): The split ratio of simcc.
-
-    Returns:
-        tuple: A tuple containing center and scale.
-        - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2)
-        - np.ndarray[float32]: scores in shape (K,) or (n, K)
-    """
-    keypoints, scores = get_simcc_maximum(simcc_x, simcc_y)
-    keypoints /= simcc_split_ratio
-
-    return keypoints, scores
-
-
-def inference_pose(session, out_bbox, oriImg):
-    h, w = session.get_inputs()[0].shape[2:]
-    model_input_size = (w, h)
-    resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size)
-    outputs = inference(session, resized_img)
-    keypoints, scores = postprocess(outputs, model_input_size, center, scale)
-
-    return keypoints, scores

invokeai/backend/image_util/dw_openpose/utils.py

@@ -1,155 +0,0 @@
-# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
-
-import math
-
-import cv2
-import matplotlib
-import numpy as np
-
-eps = 0.01
-
-
-def draw_bodypose(canvas, candidate, subset):
-    H, W, C = canvas.shape
-    candidate = np.array(candidate)
-    subset = np.array(subset)
-
-    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],
-        [3, 17],
-        [6, 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 i in range(17):
-        for n in range(len(subset)):
-            index = subset[n][np.array(limbSeq[i]) - 1]
-            if -1 in index:
-                continue
-            Y = candidate[index.astype(int), 0] * float(W)
-            X = candidate[index.astype(int), 1] * 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, colors[i])
-
-    canvas = (canvas * 0.6).astype(np.uint8)
-
-    for i in range(18):
-        for n in range(len(subset)):
-            index = int(subset[n][i])
-            if index == -1:
-                continue
-            x, y = candidate[index][0:2]
-            x = int(x * W)
-            y = int(y * H)
-            cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
-
-    return canvas
-
-
-def draw_handpose(canvas, all_hand_peaks):
-    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 peaks in all_hand_peaks:
-        peaks = np.array(peaks)
-
-        for ie, e in enumerate(edges):
-            x1, y1 = peaks[e[0]]
-            x2, y2 = peaks[e[1]]
-            x1 = int(x1 * W)
-            y1 = int(y1 * H)
-            x2 = int(x2 * W)
-            y2 = int(y2 * 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 _, keyponit in enumerate(peaks):
-            x, y = keyponit
-            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, all_lmks):
-    H, W, C = canvas.shape
-    for lmks in all_lmks:
-        lmks = np.array(lmks)
-        for lmk in lmks:
-            x, y = lmk
-            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

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -1,67 +0,0 @@
-# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
-# Modified pathing to suit Invoke
-
-import pathlib
-
-import numpy as np
-import onnxruntime as ort
-
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.backend.util.devices import choose_torch_device
-from invokeai.backend.util.util import download_with_progress_bar
-
-from .onnxdet import inference_detector
-from .onnxpose import inference_pose
-
-DWPOSE_MODELS = {
-    "yolox_l.onnx": {
-        "local": "any/annotators/dwpose/yolox_l.onnx",
-        "url": "https://huggingface.co/yzd-v/DWPose/resolve/main/yolox_l.onnx?download=true",
-    },
-    "dw-ll_ucoco_384.onnx": {
-        "local": "any/annotators/dwpose/dw-ll_ucoco_384.onnx",
-        "url": "https://huggingface.co/yzd-v/DWPose/resolve/main/dw-ll_ucoco_384.onnx?download=true",
-    },
-}
-
-config = InvokeAIAppConfig.get_config()
-
-
-class Wholebody:
-    def __init__(self):
-        device = choose_torch_device()
-
-        providers = ["CUDAExecutionProvider"] if device == "cuda" else ["CPUExecutionProvider"]
-
-        DET_MODEL_PATH = pathlib.Path(config.models_path / DWPOSE_MODELS["yolox_l.onnx"]["local"])
-        if not DET_MODEL_PATH.exists():
-            download_with_progress_bar(DWPOSE_MODELS["yolox_l.onnx"]["url"], DET_MODEL_PATH)
-
-        POSE_MODEL_PATH = pathlib.Path(config.models_path / DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["local"])
-        if not POSE_MODEL_PATH.exists():
-            download_with_progress_bar(DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["url"], POSE_MODEL_PATH)
-
-        onnx_det = DET_MODEL_PATH
-        onnx_pose = POSE_MODEL_PATH
-
-        self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers)
-        self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers)
-
-    def __call__(self, oriImg):
-        det_result = inference_detector(self.session_det, oriImg)
-        keypoints, scores = inference_pose(self.session_pose, det_result, oriImg)
-
-        keypoints_info = np.concatenate((keypoints, scores[..., None]), axis=-1)
-        # compute neck joint
-        neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
-        # neck score when visualizing pred
-        neck[:, 2:4] = np.logical_and(keypoints_info[:, 5, 2:4] > 0.3, keypoints_info[:, 6, 2:4] > 0.3).astype(int)
-        new_keypoints_info = np.insert(keypoints_info, 17, neck, axis=1)
-        mmpose_idx = [17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3]
-        openpose_idx = [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17]
-        new_keypoints_info[:, openpose_idx] = new_keypoints_info[:, mmpose_idx]
-        keypoints_info = new_keypoints_info
-
-        keypoints, scores = keypoints_info[..., :2], keypoints_info[..., 2]
-
-        return keypoints, scores

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -7,10 +7,10 @@ import cv2
 import numpy as np
 import numpy.typing as npt
 import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
 from cv2.typing import MatLike
 from tqdm import tqdm
 
-from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.util.devices import choose_torch_device
 
 """

invokeai/backend/model_management/seamless.py

@@ -1,11 +1,10 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import Callable, List, Union
+from typing import List, Union
 
 import torch.nn as nn
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
 
 
 def _conv_forward_asymmetric(self, input, weight, bias):
@@ -27,51 +26,70 @@ def _conv_forward_asymmetric(self, input, weight, bias):
 
 @contextmanager
 def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axes: List[str]):
-    # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor
-    to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = []
     try:
-        # Hard coded to skip down block layers, allowing for seamless tiling at the expense of prompt adherence
-        skipped_layers = 1
+        to_restore = []
+
         for m_name, m in model.named_modules():
-            if not isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                continue
-
-            if isinstance(model, UNet2DConditionModel) and m_name.startswith("down_blocks.") and ".resnets." in m_name:
-                # down_blocks.1.resnets.1.conv1
-                _, block_num, _, resnet_num, submodule_name = m_name.split(".")
-                block_num = int(block_num)
-                resnet_num = int(resnet_num)
-
-                if block_num >= len(model.down_blocks) - skipped_layers:
+            if isinstance(model, UNet2DConditionModel):
+                if ".attentions." in m_name:
                     continue
 
-                # Skip the second resnet (could be configurable)
-                if resnet_num > 0:
+                if ".resnets." in m_name:
+                    if ".conv2" in m_name:
+                        continue
+                    if ".conv_shortcut" in m_name:
+                        continue
+
+            """
+            if isinstance(model, UNet2DConditionModel):
+                if False and ".upsamplers." in m_name:
                     continue
 
-                # Skip Conv2d layers (could be configurable)
-                if submodule_name == "conv2":
+                if False and ".downsamplers." in m_name:
                     continue
 
-            m.asymmetric_padding_mode = {}
-            m.asymmetric_padding = {}
-            m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
-            m.asymmetric_padding["x"] = (
-                m._reversed_padding_repeated_twice[0],
-                m._reversed_padding_repeated_twice[1],
-                0,
-                0,
-            )
-            m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
-            m.asymmetric_padding["y"] = (
-                0,
-                0,
-                m._reversed_padding_repeated_twice[2],
-                m._reversed_padding_repeated_twice[3],
-            )
+                if True and ".resnets." in m_name:
+                    if True and ".conv1" in m_name:
+                        if False and "down_blocks" in m_name:
+                            continue
+                        if False and "mid_block" in m_name:
+                            continue
+                        if False and "up_blocks" in m_name:
+                            continue
 
-            to_restore.append((m, m._conv_forward))
-            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
+                    if True and ".conv2" in m_name:
+                        continue
+
+                    if True and ".conv_shortcut" in m_name:
+                        continue
+
+                if True and ".attentions." in m_name:
+                    continue
+
+                if False and m_name in ["conv_in", "conv_out"]:
+                    continue
+            """
+
+            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                m.asymmetric_padding_mode = {}
+                m.asymmetric_padding = {}
+                m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
+                m.asymmetric_padding["x"] = (
+                    m._reversed_padding_repeated_twice[0],
+                    m._reversed_padding_repeated_twice[1],
+                    0,
+                    0,
+                )
+                m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
+                m.asymmetric_padding["y"] = (
+                    0,
+                    0,
+                    m._reversed_padding_repeated_twice[2],
+                    m._reversed_padding_repeated_twice[3],
+                )
+
+                to_restore.append((m, m._conv_forward))
+                m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 

invokeai/frontend/web/package.json

@@ -52,7 +52,6 @@
     "@chakra-ui/react-use-size": "^2.1.0",
     "@dagrejs/graphlib": "^2.1.13",
     "@dnd-kit/core": "^6.1.0",
-    "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
     "@fontsource-variable/inter": "^5.0.16",
     "@invoke-ai/ui-library": "^0.0.18",

invokeai/frontend/web/pnpm-lock.yaml

@@ -22,9 +22,6 @@ dependencies:
   '@dnd-kit/core':
     specifier: ^6.1.0
     version: 6.1.0(react-dom@18.2.0)(react@18.2.0)
-  '@dnd-kit/sortable':
-    specifier: ^8.0.0
-    version: 8.0.0(@dnd-kit/core@6.1.0)(react@18.2.0)
   '@dnd-kit/utilities':
     specifier: ^3.2.2
     version: 3.2.2(react@18.2.0)
@@ -2887,18 +2884,6 @@ packages:
       tslib: 2.6.2
     dev: false
 
-  /@dnd-kit/sortable@8.0.0(@dnd-kit/core@6.1.0)(react@18.2.0):
-    resolution: {integrity: sha512-U3jk5ebVXe1Lr7c2wU7SBZjcWdQP+j7peHJfCspnA81enlu88Mgd7CC8Q+pub9ubP7eKVETzJW+IBAhsqbSu/g==}
-    peerDependencies:
-      '@dnd-kit/core': ^6.1.0
-      react: '>=16.8.0'
-    dependencies:
-      '@dnd-kit/core': 6.1.0(react-dom@18.2.0)(react@18.2.0)
-      '@dnd-kit/utilities': 3.2.2(react@18.2.0)
-      react: 18.2.0
-      tslib: 2.6.2
-    dev: false
-
   /@dnd-kit/utilities@3.2.2(react@18.2.0):
     resolution: {integrity: sha512-+MKAJEOfaBe5SmV6t34p80MMKhjvUz0vRrvVJbPT0WElzaOJ/1xs+D+KDv+tD/NE5ujfrChEcshd4fLn0wpiqg==}
     peerDependencies:

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

@@ -56,7 +56,7 @@
         "nodeEditor": "Knoten Editor",
         "statusMergingModels": "Modelle zusammenführen",
         "ipAdapter": "IP Adapter",
-        "controlAdapter": "Control-Adapter",
+        "controlAdapter": "Control Adapter",
         "auto": "Automatisch",
         "controlNet": "ControlNet",
         "imageFailedToLoad": "Kann Bild nicht laden",
@@ -69,18 +69,18 @@
         "random": "Zufall",
         "batch": "Stapel-Manager",
         "advanced": "Erweitert",
-        "unifiedCanvas": "Leinwand",
+        "unifiedCanvas": "Einheitliche Leinwand",
         "openInNewTab": "In einem neuem Tab öffnen",
         "statusProcessing": "wird bearbeitet",
         "linear": "Linear",
         "imagePrompt": "Bild Prompt",
         "checkpoint": "Checkpoint",
-        "inpaint": "Inpaint",
+        "inpaint": "inpaint",
         "simple": "Einfach",
         "template": "Vorlage",
         "outputs": "Ausgabe",
         "data": "Daten",
-        "safetensors": "Safe-Tensors",
+        "safetensors": "Safetensors",
         "outpaint": "Ausmalen",
         "details": "Details",
         "format": "Format",
@@ -127,7 +127,7 @@
         "galleryImageResetSize": "Größe zurücksetzen",
         "gallerySettings": "Galerie-Einstellungen",
         "maintainAspectRatio": "Seitenverhältnis beibehalten",
-        "autoSwitchNewImages": "Auto-Wechsel zu neuen Bildern",
+        "autoSwitchNewImages": "Automatisch zu neuen Bildern wechseln",
         "singleColumnLayout": "Einspaltiges Layout",
         "allImagesLoaded": "Alle Bilder geladen",
         "loadMore": "Mehr laden",
@@ -161,16 +161,16 @@
     "hotkeys": {
         "keyboardShortcuts": "Tastenkürzel",
         "appHotkeys": "App-Tastenkombinationen",
-        "generalHotkeys": "Allgemein",
-        "galleryHotkeys": "Galerie",
-        "unifiedCanvasHotkeys": "Leinwand",
+        "generalHotkeys": "Allgemeine Tastenkürzel",
+        "galleryHotkeys": "Galerie Tastenkürzel",
+        "unifiedCanvasHotkeys": "Unified Canvas Tastenkürzel",
         "invoke": {
             "desc": "Ein Bild erzeugen",
             "title": "Invoke"
         },
         "cancel": {
             "title": "Abbrechen",
-            "desc": "Aktuelle Bilderzeugung abbrechen"
+            "desc": "Bilderzeugung abbrechen"
         },
         "focusPrompt": {
             "title": "Fokussiere Prompt",
@@ -226,7 +226,7 @@
         },
         "sendToImageToImage": {
             "title": "An Bild zu Bild senden",
-            "desc": "Aktuelles Bild an Bild-zu-Bild senden"
+            "desc": "Aktuelles Bild an Bild zu Bild senden"
         },
         "deleteImage": {
             "title": "Bild löschen",
@@ -258,7 +258,7 @@
         },
         "selectEraser": {
             "title": "Radiergummi auswählen",
-            "desc": "Wählt den Radiergummi aus"
+            "desc": "Wählt den Radiergummi für die Leinwand aus"
         },
         "decreaseBrushSize": {
             "title": "Pinselgröße verkleinern",
@@ -330,7 +330,7 @@
         },
         "downloadImage": {
             "title": "Bild herunterladen",
-            "desc": "Aktuelles Bild herunterladen"
+            "desc": "Aktuelle Leinwand herunterladen"
         },
         "undoStroke": {
             "title": "Pinselstrich rückgängig machen",
@@ -356,7 +356,7 @@
             "title": "Staging-Bild akzeptieren",
             "desc": "Akzeptieren Sie das aktuelle Bild des Staging-Bereichs"
         },
-        "nodesHotkeys": "Knoten",
+        "nodesHotkeys": "Knoten Tastenkürzel",
         "addNodes": {
             "title": "Knotenpunkt hinzufügen",
             "desc": "Öffnet das Menü zum Hinzufügen von Knoten"
@@ -399,7 +399,7 @@
         "vaeLocation": "VAE Ort",
         "vaeLocationValidationMsg": "Pfad zum Speicherort Ihres VAE.",
         "width": "Breite",
-        "widthValidationMsg": "Standardbreite Ihres Modells.",
+        "widthValidationMsg": "Standardbreite Ihres Models.",
         "height": "Höhe",
         "heightValidationMsg": "Standardbhöhe Ihres Models.",
         "addModel": "Modell hinzufügen",
@@ -501,7 +501,7 @@
         "quickAdd": "Schnell hinzufügen",
         "simpleModelDesc": "Geben Sie einen Pfad zu einem lokalen Diffusers-Modell, einem lokalen Checkpoint-/Safetensors-Modell, einer HuggingFace-Repo-ID oder einer Checkpoint-/Diffusers-Modell-URL an.",
         "modelDeleted": "Modell gelöscht",
-        "inpainting": "V1-Inpainting",
+        "inpainting": "v1 Inpainting",
         "modelUpdateFailed": "Modellaktualisierung fehlgeschlagen",
         "useCustomConfig": "Benutzerdefinierte Konfiguration verwenden",
         "settings": "Einstellungen",
@@ -518,7 +518,7 @@
         "interpolationType": "Interpolationstyp",
         "oliveModels": "Olives",
         "variant": "Variante",
-        "loraModels": "\"LoRAs\"",
+        "loraModels": "LoRAs",
         "modelDeleteFailed": "Modell konnte nicht gelöscht werden",
         "mergedModelName": "Zusammengeführter Modellname",
         "checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
@@ -564,8 +564,8 @@
         "img2imgStrength": "Bild-zu-Bild-Stärke",
         "toggleLoopback": "Loopback umschalten",
         "sendTo": "Senden an",
-        "sendToImg2Img": "Senden an Bild-zu-Bild",
-        "sendToUnifiedCanvas": "Senden an Leinwand",
+        "sendToImg2Img": "Senden an Bild zu Bild",
+        "sendToUnifiedCanvas": "Senden an Unified Canvas",
         "copyImageToLink": "Bild-Link kopieren",
         "downloadImage": "Bild herunterladen",
         "openInViewer": "Im Viewer öffnen",
@@ -603,10 +603,7 @@
         "resetWebUIDesc2": "Wenn die Bilder nicht in der Galerie angezeigt werden oder etwas anderes nicht funktioniert, versuchen Sie bitte, die Einstellungen zurückzusetzen, bevor Sie einen Fehler auf GitHub melden.",
         "resetComplete": "Die Web-Oberfläche wurde zurückgesetzt.",
         "models": "Modelle",
-        "useSlidersForAll": "Schieberegler für alle Optionen verwenden",
-        "showAdvancedOptions": "Erweiterte Optionen anzeigen",
-        "alternateCanvasLayout": "Alternatives Leinwand-Layout",
-        "clearIntermediatesDesc1": "Das Löschen der Zwischenprodukte setzt Leinwand und ControlNet zurück."
+        "useSlidersForAll": "Schieberegler für alle Optionen verwenden"
     },
     "toast": {
         "tempFoldersEmptied": "Temp-Ordner geleert",
@@ -620,7 +617,7 @@
         "imageSavedToGallery": "Bild in die Galerie gespeichert",
         "canvasMerged": "Leinwand zusammengeführt",
         "sentToImageToImage": "Gesendet an Bild zu Bild",
-        "sentToUnifiedCanvas": "Gesendet an Leinwand",
+        "sentToUnifiedCanvas": "Gesendet an Unified Canvas",
         "parametersSet": "Parameter festlegen",
         "parametersNotSet": "Parameter nicht festgelegt",
         "parametersNotSetDesc": "Keine Metadaten für dieses Bild gefunden.",
@@ -637,21 +634,7 @@
         "metadataLoadFailed": "Metadaten konnten nicht geladen werden",
         "initialImageSet": "Ausgangsbild festgelegt",
         "initialImageNotSet": "Ausgangsbild nicht festgelegt",
-        "initialImageNotSetDesc": "Ausgangsbild konnte nicht geladen werden",
-        "setCanvasInitialImage": "Ausgangsbild setzen",
-        "problemMergingCanvas": "Problem bei Verschmelzung der Leinwand",
-        "canvasCopiedClipboard": "Leinwand in Zwischenablage kopiert",
-        "canvasSentControlnetAssets": "Leinwand an ControlNet & Sammlung geschickt",
-        "problemDownloadingCanvasDesc": "Kann Basis-Layer nicht exportieren",
-        "canvasDownloaded": "Leinwand heruntergeladen",
-        "problemSavingCanvasDesc": "Kann Basis-Layer nicht exportieren",
-        "canvasSavedGallery": "Leinwand in Galerie gespeichert",
-        "problemMergingCanvasDesc": "Kann Basis-Layer nicht exportieren",
-        "problemSavingCanvas": "Problem beim Speichern der Leinwand",
-        "problemCopyingCanvas": "Problem beim Kopieren der Leinwand",
-        "problemCopyingCanvasDesc": "Kann Basis-Layer nicht exportieren",
-        "problemDownloadingCanvas": "Problem beim Herunterladen der Leinwand",
-        "setAsCanvasInitialImage": "Als Ausgangsbild gesetzt"
+        "initialImageNotSetDesc": "Ausgangsbild konnte nicht geladen werden"
     },
     "tooltip": {
         "feature": {
@@ -663,8 +646,8 @@
             "upscale": "Verwenden Sie ESRGAN, um das Bild unmittelbar nach der Erzeugung zu vergrößern.",
             "faceCorrection": "Gesichtskorrektur mit GFPGAN oder Codeformer: Der Algorithmus erkennt Gesichter im Bild und korrigiert alle Fehler. Ein hoher Wert verändert das Bild stärker, was zu attraktiveren Gesichtern führt. Codeformer mit einer höheren Genauigkeit bewahrt das Originalbild auf Kosten einer stärkeren Gesichtskorrektur.",
             "imageToImage": "Bild zu Bild lädt ein beliebiges Bild als Ausgangsbild, aus dem dann zusammen mit dem Prompt ein neues Bild erzeugt wird. Je höher der Wert ist, desto stärker wird das Ergebnisbild verändert. Werte von 0,0 bis 1,0 sind möglich, der empfohlene Bereich ist .25-.75",
-            "boundingBox": "Der Begrenzungsrahmen ist derselbe wie die Einstellungen für Breite und Höhe bei Text-zu-Bild oder Bild-zu-Bild. Es wird nur der Bereich innerhalb des Rahmens verarbeitet.",
-            "seamCorrection": "Behandlung von sichtbaren Übergängen, die zwischen den erzeugten Bildern auftreten.",
+            "boundingBox": "Der Begrenzungsrahmen ist derselbe wie die Einstellungen für Breite und Höhe bei Text zu Bild oder Bild zu Bild. Es wird nur der Bereich innerhalb des Rahmens verarbeitet.",
+            "seamCorrection": "Steuert die Behandlung von sichtbaren Übergängen, die zwischen den erzeugten Bildern auf der Leinwand auftreten.",
             "infillAndScaling": "Verwalten Sie Infill-Methoden (für maskierte oder gelöschte Bereiche der Leinwand) und Skalierung (nützlich für kleine Begrenzungsrahmengrößen)."
         }
     },
@@ -675,17 +658,17 @@
         "maskingOptions": "Maskierungsoptionen",
         "enableMask": "Maske aktivieren",
         "preserveMaskedArea": "Maskierten Bereich bewahren",
-        "clearMask": "Maske löschen (Shift+C)",
+        "clearMask": "Maske löschen",
         "brush": "Pinsel",
         "eraser": "Radierer",
         "fillBoundingBox": "Begrenzungsrahmen füllen",
         "eraseBoundingBox": "Begrenzungsrahmen löschen",
-        "colorPicker": "Pipette",
+        "colorPicker": "Farbpipette",
         "brushOptions": "Pinseloptionen",
         "brushSize": "Größe",
         "move": "Bewegen",
         "resetView": "Ansicht zurücksetzen",
-        "mergeVisible": "Sichtbare zusammenführen",
+        "mergeVisible": "Sichtbare Zusammenführen",
         "saveToGallery": "In Galerie speichern",
         "copyToClipboard": "In Zwischenablage kopieren",
         "downloadAsImage": "Als Bild herunterladen",
@@ -699,15 +682,15 @@
         "darkenOutsideSelection": "Außerhalb der Auswahl verdunkeln",
         "autoSaveToGallery": "Automatisch in Galerie speichern",
         "saveBoxRegionOnly": "Nur Auswahlbox speichern",
-        "limitStrokesToBox": "Striche auf Auswahl beschränken",
-        "showCanvasDebugInfo": "Zusätzliche Informationen anzeigen",
+        "limitStrokesToBox": "Striche auf Box beschränken",
+        "showCanvasDebugInfo": "Zusätzliche Informationen zur Leinwand anzeigen",
         "clearCanvasHistory": "Leinwand-Verlauf löschen",
         "clearHistory": "Verlauf löschen",
-        "clearCanvasHistoryMessage": "Wenn Sie den Verlauf löschen, bleibt die aktuelle Leinwand intakt, aber der Verlauf der Rückgängig- und Wiederherstellung wird unwiderruflich gelöscht.",
-        "clearCanvasHistoryConfirm": "Sind Sie sicher, dass Sie den Verlauf löschen möchten?",
+        "clearCanvasHistoryMessage": "Wenn Sie den Verlauf der Leinwand löschen, bleibt die aktuelle Leinwand intakt, aber der Verlauf der Rückgängig- und Wiederherstellung wird unwiderruflich gelöscht.",
+        "clearCanvasHistoryConfirm": "Sind Sie sicher, dass Sie den Verlauf der Leinwand löschen möchten?",
         "emptyTempImageFolder": "Temp-Image Ordner leeren",
         "emptyFolder": "Leerer Ordner",
-        "emptyTempImagesFolderMessage": "Wenn Sie den Ordner für temporäre Bilder leeren, wird die Leinwand zurückgesetzt. Dies umfasst den gesamten Verlauf der Rückgängig-/Wiederherstellungsvorgänge, die Bilder im Bereitstellungsbereich und die Leinwand-Basisebene.",
+        "emptyTempImagesFolderMessage": "Wenn Sie den Ordner für temporäre Bilder leeren, wird auch der Unified Canvas vollständig zurückgesetzt. Dies umfasst den gesamten Verlauf der Rückgängig-/Wiederherstellungsvorgänge, die Bilder im Bereitstellungsbereich und die Leinwand-Basisebene.",
         "emptyTempImagesFolderConfirm": "Sind Sie sicher, dass Sie den temporären Ordner leeren wollen?",
         "activeLayer": "Aktive Ebene",
         "canvasScale": "Leinwand Maßstab",
@@ -724,14 +707,14 @@
         "discardAll": "Alles verwerfen",
         "betaClear": "Löschen",
         "betaDarkenOutside": "Außen abdunkeln",
-        "betaLimitToBox": "Auf Auswahl begrenzen",
+        "betaLimitToBox": "Begrenzung auf das Feld",
         "betaPreserveMasked": "Maskiertes bewahren",
         "antialiasing": "Kantenglättung",
         "showResultsOn": "Zeige Ergebnisse (An)",
         "showResultsOff": "Zeige Ergebnisse (Aus)"
     },
     "accessibility": {
-        "modelSelect": "Modell-Auswahl",
+        "modelSelect": "Model Auswahl",
         "uploadImage": "Bild hochladen",
         "previousImage": "Voriges Bild",
         "useThisParameter": "Benutze diesen Parameter",
@@ -743,11 +726,11 @@
         "modifyConfig": "Optionen einstellen",
         "toggleAutoscroll": "Auroscroll ein/ausschalten",
         "toggleLogViewer": "Log Betrachter ein/ausschalten",
-        "showOptionsPanel": "Seitenpanel anzeigen",
+        "showOptionsPanel": "Zeige Optionen",
         "reset": "Zurücksetzten",
         "nextImage": "Nächstes Bild",
         "zoomOut": "Verkleinern",
-        "rotateCounterClockwise": "Gegen den Uhrzeigersinn drehen",
+        "rotateCounterClockwise": "Gegen den Uhrzeigersinn verdrehen",
         "showGalleryPanel": "Galeriefenster anzeigen",
         "exitViewer": "Betrachten beenden",
         "menu": "Menü",
@@ -762,14 +745,14 @@
         "autoAddBoard": "Automatisches Hinzufügen zum Ordner",
         "topMessage": "Dieser Ordner enthält Bilder die in den folgenden Funktionen verwendet werden:",
         "move": "Bewegen",
-        "menuItemAutoAdd": "Auto-Hinzufügen zu diesem Ordner",
+        "menuItemAutoAdd": "Automatisches Hinzufügen zu diesem Ordner",
         "myBoard": "Meine Ordner",
         "searchBoard": "Ordner durchsuchen...",
         "noMatching": "Keine passenden Ordner",
         "selectBoard": "Ordner aussuchen",
         "cancel": "Abbrechen",
         "addBoard": "Ordner hinzufügen",
-        "uncategorized": "Ohne Kategorie",
+        "uncategorized": "Nicht kategorisiert",
         "downloadBoard": "Ordner runterladen",
         "changeBoard": "Ordner wechseln",
         "loading": "Laden...",
@@ -801,7 +784,7 @@
         "depthMidasDescription": "Tiefenmap erstellen mit Midas",
         "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ist aktiv, $t(common.controlNet) ist deaktiviert",
-        "weight": "Einfluss",
+        "weight": "Breite",
         "selectModel": "Wähle ein Modell",
         "depthMidas": "Tiefe (Midas)",
         "w": "W",
@@ -826,14 +809,14 @@
         "controlAdapter_other": "Control Adapter",
         "colorMapTileSize": "Kachelgröße",
         "depthZoeDescription": "Tiefenmap erstellen mit Zoe",
-        "setControlImageDimensions": "Setze Control-Bild Auflösung auf Breite/Höhe",
+        "setControlImageDimensions": "Setze Control Bild Auflösung auf Breite/Höhe",
         "handAndFace": "Hand und Gesicht",
         "enableIPAdapter": "Aktiviere IP Adapter",
         "resize": "Größe ändern",
         "resetControlImage": "Zurücksetzen vom Referenz Bild",
         "balanced": "Ausgewogen",
         "prompt": "Prompt",
-        "resizeMode": "Größe",
+        "resizeMode": "Größenänderungsmodus",
         "processor": "Prozessor",
         "saveControlImage": "Speichere Referenz Bild",
         "safe": "Speichern",
@@ -842,6 +825,7 @@
         "pidi": "PIDI",
         "normalBae": "Normales BAE",
         "mlsdDescription": "Minimalistischer Liniensegmentdetektor",
+        "openPoseDescription": "Schätzung der menschlichen Pose mit Openpose",
         "control": "Kontrolle",
         "coarse": "Grob",
         "crop": "Zuschneiden",
@@ -854,12 +838,13 @@
         "lineartAnimeDescription": "Lineart-Verarbeitung im Anime-Stil",
         "minConfidence": "Minimales Vertrauen",
         "megaControl": "Mega-Kontrolle",
-        "autoConfigure": "Prozessor Auto-konfig",
+        "autoConfigure": "Prozessor automatisch konfigurieren",
         "normalBaeDescription": "Normale BAE-Verarbeitung",
         "noneDescription": "Es wurde keine Verarbeitung angewendet",
-        "lineartAnime": "Lineart Anime / \"Strichzeichnung Anime\"",
+        "openPose": "Openpose",
+        "lineartAnime": "Lineart Anime",
         "mediapipeFaceDescription": "Gesichtserkennung mit Mediapipe",
-        "canny": "\"Canny\"",
+        "canny": "Canny",
         "hedDescription": "Ganzheitlich verschachtelte Kantenerkennung",
         "scribble": "Scribble",
         "maxFaces": "Maximale Anzahl Gesichter",
@@ -868,7 +853,7 @@
         "modelSize": "Modell Größe",
         "small": "Klein",
         "base": "Basis",
-        "depthAnything": "Depth Anything / \"Tiefe irgendwas\"",
+        "depthAnything": "Depth Anything",
         "depthAnythingDescription": "Erstellung einer Tiefenkarte mit der Depth Anything-Technik"
     },
     "queue": {
@@ -930,9 +915,7 @@
         "openQueue": "Warteschlange öffnen",
         "batchFailedToQueue": "Fehler beim Einreihen in die Stapelverarbeitung",
         "batchFieldValues": "Stapelverarbeitungswerte",
-        "batchQueued": "Stapelverarbeitung eingereiht",
-        "graphQueued": "Graph eingereiht",
-        "graphFailedToQueue": "Fehler beim Einreihen des Graphen"
+        "batchQueued": "Stapelverarbeitung eingereiht"
     },
     "metadata": {
         "negativePrompt": "Negativ Beschreibung",
@@ -953,144 +936,46 @@
         "generationMode": "Generierungsmodus",
         "Threshold": "Rauschen-Schwelle",
         "seed": "Seed",
-        "perlin": "Perlin-Rauschen",
+        "perlin": "Perlin Noise",
         "hiresFix": "Optimierung für hohe Auflösungen",
         "initImage": "Erstes Bild",
-        "variations": "Seed-Gewichtungs-Paare",
+        "variations": "Samengewichtspaare",
         "vae": "VAE",
-        "workflow": "Workflow",
+        "workflow": "Arbeitsablauf",
         "scheduler": "Planer",
         "noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden",
-        "recallParameters": "Parameter wiederherstellen"
+        "recallParameters": "Recall Parameters"
     },
     "popovers": {
         "noiseUseCPU": {
-            "heading": "Nutze Prozessor rauschen",
-            "paragraphs": [
-                "Entscheidet, ob auf der CPU oder GPU Rauschen erzeugt wird.",
-                "Mit aktiviertem CPU-Rauschen wird ein bestimmter Seedwert das gleiche Bild auf jeder Maschine erzeugen.",
-                "CPU-Rauschen einzuschalten beeinflusst nicht die Systemleistung."
-            ]
+            "heading": "Nutze Prozessor rauschen"
         },
         "paramModel": {
-            "heading": "Modell",
-            "paragraphs": [
-                "Modell für die Entrauschungsschritte.",
-                "Verschiedene Modelle werden in der Regel so trainiert, dass sie sich auf die Erzeugung bestimmter Ästhetik und/oder Inhalte spezialisiert."
-            ]
+            "heading": "Modell"
         },
         "paramIterations": {
-            "heading": "Iterationen",
-            "paragraphs": [
-                "Die Anzahl der Bilder, die erzeugt werden sollen.",
-                "Wenn \"Dynamische Prompts\" aktiviert ist, wird jeder einzelne Prompt so oft generiert."
-            ]
+            "heading": "Iterationen"
         },
         "paramCFGScale": {
-            "heading": "CFG-Skala",
-            "paragraphs": [
-                "Bestimmt, wie viel Ihr Prompt den Erzeugungsprozess beeinflusst."
-            ]
+            "heading": "CFG-Skala"
         },
         "paramSteps": {
-            "heading": "Schritte",
-            "paragraphs": [
-                "Anzahl der Schritte, die bei jeder Generierung durchgeführt werden.",
-                "Höhere Schrittzahlen werden in der Regel bessere Bilder ergeben, aber mehr Zeit benötigen."
-            ]
+            "heading": "Schritte"
         },
         "lora": {
-            "heading": "LoRA Gewichte",
-            "paragraphs": [
-                "Höhere LoRA-Wichtungen führen zu größeren Auswirkungen auf das endgültige Bild."
-            ]
+            "heading": "LoRA Gewichte"
         },
         "infillMethod": {
-            "heading": "Füllmethode",
-            "paragraphs": [
-                "Infill-Methode für den ausgewählten Bereich."
-            ]
+            "heading": "Füllmethode"
         },
         "paramVAE": {
-            "heading": "VAE",
-            "paragraphs": [
-                "Verwendetes Modell, um den KI-Ausgang in das endgültige Bild zu übersetzen."
-            ]
-        },
-        "paramRatio": {
-            "heading": "Seitenverhältnis",
-            "paragraphs": [
-                "Das Seitenverhältnis des erzeugten Bildes.",
-                "Für SD1.5-Modelle wird eine Bildgröße von 512x512 Pixel empfohlen, für SDXL-Modelle sind es 1024x1024 Pixel."
-            ]
-        },
-        "paramDenoisingStrength": {
-            "paragraphs": [
-                "Wie viel Rauschen dem Eingabebild hinzugefügt wird.",
-                "0 wird zu einem identischen Bild führen, während 1 zu einem völlig neuen Bild führt."
-            ],
-            "heading": "Stärke der Entrauschung"
-        },
-        "paramVAEPrecision": {
-            "heading": "VAE-Präzision",
-            "paragraphs": [
-                "Die bei der VAE-Kodierung und Dekodierung verwendete Präzision. FP16/Halbpräzision ist effizienter, aber auf Kosten kleiner Bildvariationen."
-            ]
-        },
-        "paramCFGRescaleMultiplier": {
-            "heading": "CFG Rescale Multiplikator",
-            "paragraphs": [
-                "Rescale-Multiplikator für die CFG-Lenkung, der für Modelle verwendet wird, die mit dem zero-terminal SNR (ztsnr) trainiert wurden. Empfohlener Wert: 0,7."
-            ]
-        },
-        "scaleBeforeProcessing": {
-            "paragraphs": [
-                "Skaliert den ausgewählten Bereich auf die Größe, die für das Modell am besten geeignet ist."
-            ],
-            "heading": "Skalieren vor der Verarbeitung"
-        },
-        "paramSeed": {
-            "paragraphs": [
-                "Kontrolliert das für die Erzeugung verwendete Startrauschen.",
-                "Deaktivieren Sie “Random Seed”, um identische Ergebnisse mit den gleichen Generierungseinstellungen zu erzeugen."
-            ],
-            "heading": "Seed"
-        },
-        "dynamicPromptsMaxPrompts": {
-            "paragraphs": [
-                "Beschränkt die Anzahl der Prompts, die von \"Dynamic Prompts\" generiert werden können."
-            ],
-            "heading": "Maximale Prompts"
-        },
-        "dynamicPromptsSeedBehaviour": {
-            "paragraphs": [
-                "Bestimmt, wie der Seed-Wert beim Erzeugen von Prompts verwendet wird.",
-                "Verwenden Sie dies, um schnelle Variationen eines einzigen Seeds zu erkunden.",
-                "Wenn Sie z. B. 5 Prompts haben, wird jedes Bild den selben Seed-Wert verwenden.",
-                "\"Per Bild\" wird einen einzigartigen Seed-Wert für jedes Bild verwenden. Dies bietet mehr Variationen."
-            ],
-            "heading": "Seed-Verhalten"
-        },
-        "dynamicPrompts": {
-            "paragraphs": [
-                "\"Dynamische Prompts\" übersetzt einen Prompt in mehrere.",
-                "Die Ausgangs-Syntax ist \"ein {roter|grüner|blauer} ball\". Das generiert 3 Prompts: \"ein roter ball\", \"ein grüner ball\" und \"ein blauer ball\".",
-                "Sie können die Syntax so oft verwenden, wie Sie in einem einzigen Prompt möchten, aber stellen Sie sicher, dass die Anzahl der Prompts zur Einstellung von \"Max Prompts\" passt."
-            ],
-            "heading": "Dynamische Prompts"
-        },
-        "controlNetWeight": {
-            "paragraphs": [
-                "Wie stark wird das ControlNet das generierte Bild beeinflussen wird."
-            ],
-            "heading": "Einfluss"
+            "heading": "VAE"
         }
     },
     "ui": {
         "lockRatio": "Verhältnis sperren",
         "hideProgressImages": "Verstecke Prozess Bild",
-        "showProgressImages": "Zeige Prozess Bild",
-        "swapSizes": "Tausche Größen"
+        "showProgressImages": "Zeige Prozess Bild"
     },
     "invocationCache": {
         "disable": "Deaktivieren",
@@ -1104,7 +989,7 @@
         "enableFailed": "Problem beim Aktivieren des Zwischenspeichers",
         "disableFailed": "Problem bei Deaktivierung des Cache",
         "enableSucceeded": "Zwischenspeicher aktiviert",
-        "disableSucceeded": "Invocation-Cache deaktiviert",
+        "disableSucceeded": "Aufrufcache deaktiviert",
         "clearSucceeded": "Zwischenspeicher gelöscht",
         "invocationCache": "Zwischenspeicher",
         "clearFailed": "Problem beim Löschen des Zwischenspeichers"
@@ -1150,15 +1035,15 @@
         "collectionFieldType": "{{name}} Sammlung",
         "controlCollectionDescription": "Kontrollinformationen zwischen Knotenpunkten weitergegeben.",
         "connectionWouldCreateCycle": "Verbindung würde einen Kreislauf/cycle schaffen",
-        "ipAdapterDescription": "Ein Adapter für die Bildabfrage (IP-Adapter) / Bildprompt-Adapter.",
+        "ipAdapterDescription": "Ein Adapter für die Bildabfrage (IP-Adapter) / Bilderprompt-Adapter.",
         "controlField": "Kontrolle",
         "inputFields": "Eingabefelder",
         "imageField": "Bild",
         "inputMayOnlyHaveOneConnection": "Eingang darf nur eine Verbindung haben",
         "integerCollectionDescription": "Eine Sammlung ganzer Zahlen.",
-        "integerDescription": "\"Integer\" sind ganze Zahlen ohne Dezimalpunkt.",
+        "integerDescription": "Das sind ganze Zahlen ohne Dezimalpunkt.",
         "conditioningPolymorphic": "Konditionierung polymorphisch",
-        "conditioningPolymorphicDescription": "Die Konditionierung kann zwischen den Knoten weitergegeben werden.",
+        "conditioningPolymorphicDescription": "Die Konditionierung kann zwischen den Knotenpunkten weitergegeben werden.",
         "invalidOutputSchema": "Ungültiges Ausgabeschema",
         "ipAdapterModel": "IP-Adapter Modell",
         "conditioningFieldDescription": "Die Konditionierung kann zwischen den Knotenpunkten weitergegeben werden.",
@@ -1180,117 +1065,10 @@
         "imageCollection": "Bildersammlung",
         "imageCollectionDescription": "Eine Sammlung von Bildern.",
         "denoiseMaskField": "Entrauschen-Maske",
-        "ipAdapterCollection": "IP-Adapter Sammlung",
-        "newWorkflowDesc2": "Ihr aktueller Arbeitsablauf hat ungespeicherte Änderungen.",
-        "problemSettingTitle": "Problem beim Einstellen des Titels",
-        "noConnectionData": "Keine Verbindungsdaten",
-        "outputField": "Ausgabefeld",
-        "outputFieldInInput": "Ausgabefeld im Eingang",
-        "problemReadingWorkflow": "Problem beim Lesen des Arbeitsablaufs vom Bild",
-        "reloadNodeTemplates": "Knoten-Vorlagen neu laden",
-        "newWorkflow": "Neuer Arbeitsablauf / Workflow",
-        "newWorkflowDesc": "Einen neuen Arbeitsablauf erstellen?",
-        "noFieldsLinearview": "Keine Felder zur linearen Ansicht hinzugefügt",
-        "clearWorkflow": "Workflow löschen",
-        "clearWorkflowDesc": "Diesen Arbeitsablauf löschen und neu starten?",
-        "noConnectionInProgress": "Es besteht keine Verbindung",
-        "notes": "Anmerkungen",
-        "nodeVersion": "Knoten Version",
-        "noOutputSchemaName": "Kein Name des Ausgabeschemas im ref-Objekt gefunden",
-        "node": "Knoten",
-        "nodeSearch": "Knoten suchen",
-        "removeLinearView": "Entfernen aus Linear View",
-        "nodeOutputs": "Knoten-Ausgänge",
-        "nodeTemplate": "Knoten-Vorlage",
-        "nodeType": "Knotentyp",
-        "noFieldType": "Kein Feldtyp",
-        "oNNXModelField": "ONNX-Modell",
-        "noMatchingNodes": "Keine passenden Knoten",
-        "noNodeSelected": "Kein Knoten gewählt",
-        "noImageFoundState": "Kein Anfangsbild im Status gefunden",
-        "nodeOpacity": "Knoten-Deckkraft",
-        "noOutputRecorded": "Keine Ausgänge aufgezeichnet",
-        "outputSchemaNotFound": "Ausgabeschema nicht gefunden",
-        "oNNXModelFieldDescription": "ONNX-Modellfeld.",
-        "outputNode": "Ausgabeknoten",
-        "pickOne": "Eins auswählen",
-        "problemReadingMetadata": "Problem beim Lesen von Metadaten aus dem Bild",
-        "notesDescription": "Anmerkungen zum Arbeitsablauf hinzufügen",
-        "outputFields": "Ausgabefelder",
-        "sDXLRefinerModelField": "Refiner-Modell",
-        "sDXLMainModelFieldDescription": "SDXL Modellfeld.",
-        "clearWorkflowDesc2": "Ihr aktueller Arbeitsablauf hat ungespeicherte Änderungen.",
-        "skipped": "Übersprungen",
-        "schedulerDescription": "Zu erledigen",
-        "scheduler": "Planer",
-        "showGraphNodes": "Graph Overlay anzeigen",
-        "showMinimapnodes": "MiniMap anzeigen",
-        "sDXLMainModelField": "SDXL Modell",
-        "skippedReservedInput": "Reserviertes Eingabefeld übersprungen",
-        "sDXLRefinerModelFieldDescription": "Zu erledigen",
-        "showLegendNodes": "Feldtyp-Legende anzeigen",
-        "skippedReservedOutput": "Reserviertes Ausgangsfeld übersprungen",
-        "skippingInputNoTemplate": "Überspringe Eingabefeld ohne Vorlage",
-        "executionStateCompleted": "Erledigt",
-        "denoiseMaskFieldDescription": "Denoise Maske kann zwischen Knoten weitergegeben werden",
-        "downloadWorkflow": "Workflow JSON herunterladen",
-        "executionStateInProgress": "In Bearbeitung",
-        "snapToGridHelp": "Knoten am Gitternetz einrasten bei Bewegung",
-        "controlCollection": "Control-Sammlung",
-        "controlFieldDescription": "Control-Informationen zwischen Knotenpunkten weitergegeben.",
-        "latentsField": "Latents",
-        "mainModelFieldDescription": "Zu erledigen",
-        "missingTemplate": "Ungültiger Knoten: Knoten {{node}} vom Typ {{type}} fehlt Vorlage (nicht installiert?)",
-        "skippingUnknownInputType": "Überspringe unbekannten Eingabe-Feldtyp",
-        "stringCollectionDescription": "Eine Sammlung von Zeichenfolgen.",
-        "string": "Zeichenfolge",
-        "stringCollection": "Sammlung von Zeichenfolgen",
-        "stringDescription": "Zeichenfolgen (Strings) sind Text.",
-        "fieldTypesMustMatch": "Feldtypen müssen übereinstimmen",
-        "fitViewportNodes": "An Ansichtsgröße anpassen",
-        "missingCanvaInitMaskImages": "Fehlende Startbilder und Masken auf der Leinwand",
-        "missingCanvaInitImage": "Fehlendes Startbild auf der Leinwand",
-        "ipAdapterModelDescription": "IP-Adapter-Modellfeld",
-        "latentsPolymorphicDescription": "Zwischen Nodes können Latents weitergegeben werden.",
-        "loadingNodes": "Lade Nodes...",
-        "latentsCollectionDescription": "Zwischen Knoten können Latents weitergegeben werden.",
-        "mismatchedVersion": "Ungültiger Knoten: Knoten {{node}} vom Typ {{type}} hat keine passende Version (Update versuchen?)",
-        "colorCollectionDescription": "Zu erledigen",
-        "ipAdapterPolymorphicDescription": "Eine Sammlung von IP-Adaptern.",
-        "fullyContainNodesHelp": "Nodes müssen vollständig innerhalb der Auswahlbox sein, um ausgewählt werden zu können",
-        "latentsFieldDescription": "Zwischen Nodes können Latents weitergegeben werden.",
-        "noWorkflow": "Kein Workflow",
-        "hideGraphNodes": "Graph Overlay verbergen",
-        "sourceNode": "Quellknoten",
-        "executionStateError": "Fehler",
-        "latentsCollection": "Latents Sammlung",
-        "maybeIncompatible": "Möglicherweise inkompatibel mit installierten",
-        "nodePack": "Knoten-Pack",
-        "skippingUnknownOutputType": "Überspringe unbekannten Ausgabe-Feldtyp",
-        "loadWorkflow": "Lade Workflow",
-        "snapToGrid": "Am Gitternetz einrasten",
-        "skippingReservedFieldType": "Überspringe reservierten Feldtyp",
-        "loRAModelField": "LoRA",
-        "loRAModelFieldDescription": "Zu erledigen",
-        "mainModelField": "Modell",
-        "doesNotExist": "existiert nicht",
-        "vaeField": "VAE",
-        "unknownOutput": "Unbekannte Ausgabe: {{name}}",
-        "updateNode": "Knoten updaten",
-        "edge": "Rand / Kante",
-        "sourceNodeDoesNotExist": "Ungültiger Rand: Quell- / Ausgabe-Knoten {{node}} existiert nicht",
-        "updateAllNodes": "Update Knoten",
-        "allNodesUpdated": "Alle Knoten aktualisiert",
-        "unknownTemplate": "Unbekannte Vorlage",
-        "floatDescription": "Floats sind Zahlen mit einem Dezimalpunkt.",
-        "updateApp": "Update App",
-        "vaeFieldDescription": "VAE Submodell.",
-        "unknownInput": "Unbekannte Eingabe: {{name}}",
-        "unknownNodeType": "Unbekannter Knotentyp",
-        "float": "Kommazahlen"
+        "ipAdapterCollection": "IP-Adapter Sammlung"
     },
     "hrf": {
-        "enableHrf": "Korrektur für hohe Auflösungen",
+        "enableHrf": "Aktivieren Sie die Korrektur für hohe Auflösungen",
         "upscaleMethod": "Vergrößerungsmethoden",
         "enableHrfTooltip": "Generieren Sie mit einer niedrigeren Anfangsauflösung, skalieren Sie auf die Basisauflösung hoch und führen Sie dann Image-to-Image aus.",
         "metadata": {
@@ -1349,7 +1127,7 @@
         "workflows": "Arbeitsabläufe",
         "noSystemWorkflows": "Keine System-Arbeitsabläufe",
         "workflowName": "Arbeitsablauf-Name",
-        "workflowIsOpen": "Arbeitsablauf ist geöffnet",
+        "workflowIsOpen": "Arbeitsablauf ist offen",
         "saveWorkflowAs": "Arbeitsablauf speichern als",
         "searchWorkflows": "Suche Arbeitsabläufe",
         "newWorkflowCreated": "Neuer Arbeitsablauf erstellt",

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

@@ -175,7 +175,6 @@
         "statusUpscaling": "Upscaling",
         "statusUpscalingESRGAN": "Upscaling (ESRGAN)",
         "template": "Template",
-        "toResolve": "To resolve",
         "training": "Training",
         "trainingDesc1": "A dedicated workflow for training your own embeddings and checkpoints using Textual Inversion and Dreambooth from the web interface.",
         "trainingDesc2": "InvokeAI already supports training custom embeddourings using Textual Inversion using the main script.",
@@ -236,9 +235,6 @@
         "fill": "Fill",
         "h": "H",
         "handAndFace": "Hand and Face",
-        "face": "Face",
-        "body": "Body",
-        "hands": "Hands",
         "hed": "HED",
         "hedDescription": "Holistically-Nested Edge Detection",
         "hideAdvanced": "Hide Advanced",
@@ -265,8 +261,8 @@
         "noneDescription": "No processing applied",
         "normalBae": "Normal BAE",
         "normalBaeDescription": "Normal BAE processing",
-        "dwOpenpose": "DW Openpose",
-        "dwOpenposeDescription": "Human pose estimation using DW Openpose",
+        "openPose": "Openpose",
+        "openPoseDescription": "Human pose estimation using Openpose",
         "pidi": "PIDI",
         "pidiDescription": "PIDI image processing",
         "processor": "Processor",
@@ -901,7 +897,6 @@
         "doesNotExist": "does not exist",
         "downloadWorkflow": "Download Workflow JSON",
         "edge": "Edge",
-        "editMode": "Edit in Workflow Editor",
         "enum": "Enum",
         "enumDescription": "Enums are values that may be one of a number of options.",
         "executionStateCompleted": "Completed",
@@ -997,10 +992,8 @@
         "problemReadingMetadata": "Problem reading metadata from image",
         "problemReadingWorkflow": "Problem reading workflow from image",
         "problemSettingTitle": "Problem Setting Title",
-        "resetToDefaultValue": "Reset to default value",
         "reloadNodeTemplates": "Reload Node Templates",
         "removeLinearView": "Remove from Linear View",
-        "reorderLinearView": "Reorder Linear View",
         "newWorkflow": "New Workflow",
         "newWorkflowDesc": "Create a new workflow?",
         "newWorkflowDesc2": "Your current workflow has unsaved changes.",
@@ -1071,7 +1064,6 @@
         "vaeModelFieldDescription": "TODO",
         "validateConnections": "Validate Connections and Graph",
         "validateConnectionsHelp": "Prevent invalid connections from being made, and invalid graphs from being invoked",
-        "viewMode": "Use in Linear View",
         "unableToGetWorkflowVersion": "Unable to get workflow schema version",
         "unrecognizedWorkflowVersion": "Unrecognized workflow schema version {{version}}",
         "version": "Version",

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

@@ -795,8 +795,7 @@
         "workflowDeleted": "Flusso di lavoro eliminato",
         "problemRetrievingWorkflow": "Problema nel recupero del flusso di lavoro",
         "resetInitialImage": "Reimposta l'immagine iniziale",
-        "uploadInitialImage": "Carica l'immagine iniziale",
-        "problemDownloadingImage": "Impossibile scaricare l'immagine"
+        "uploadInitialImage": "Carica l'immagine iniziale"
     },
     "tooltip": {
         "feature": {
@@ -1135,10 +1134,7 @@
         "newWorkflow": "Nuovo flusso di lavoro",
         "newWorkflowDesc": "Creare un nuovo flusso di lavoro?",
         "newWorkflowDesc2": "Il flusso di lavoro attuale presenta modifiche non salvate.",
-        "unsupportedAnyOfLength": "unione di troppi elementi ({{count}})",
-        "clearWorkflowDesc": "Cancellare questo flusso di lavoro e avviarne uno nuovo?",
-        "clearWorkflow": "Cancella il flusso di lavoro",
-        "clearWorkflowDesc2": "Il tuo flusso di lavoro attuale presenta modifiche non salvate."
+        "unsupportedAnyOfLength": "unione di troppi elementi ({{count}})"
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1195,6 +1191,7 @@
         "f": "F",
         "h": "A",
         "prompt": "Prompt",
+        "openPoseDescription": "Stima della posa umana utilizzando Openpose",
         "resizeMode": "Ridimensionamento",
         "weight": "Peso",
         "selectModel": "Seleziona un modello",
@@ -1675,9 +1672,7 @@
         "downloadWorkflow": "Salva su file",
         "uploadWorkflow": "Carica da file",
         "projectWorkflows": "Flussi di lavoro del progetto",
-        "noWorkflows": "Nessun flusso di lavoro",
-        "workflowCleared": "Flusso di lavoro cancellato",
-        "saveWorkflowToProject": "Salva flusso di lavoro nel progetto"
+        "noWorkflows": "Nessun flusso di lavoro"
     },
     "app": {
         "storeNotInitialized": "Il negozio non è inizializzato"

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

@@ -555,6 +555,7 @@
         "balanced": "バランス",
         "prompt": "プロンプト",
         "depthMidasDescription": "Midasを使用して深度マップを生成",
+        "openPoseDescription": "Openposeを使用してポーズを推定",
         "control": "コントロール",
         "resizeMode": "リサイズモード",
         "weight": "重み",

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

@@ -333,6 +333,7 @@
         "h": "H",
         "prompt": "프롬프트",
         "depthMidasDescription": "Midas를 사용하여 Depth map 생성하기",
+        "openPoseDescription": "Openpose를 이용한 사람 포즈 추정",
         "control": "Control",
         "resizeMode": "크기 조정 모드",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) 사용 가능,$t(common.controlNet) 사용 불가능",
@@ -369,6 +370,7 @@
         "normalBaeDescription": "Normal BAE 처리",
         "noneDescription": "처리되지 않음",
         "saveControlImage": "Control Image 저장",
+        "openPose": "Openpose",
         "toggleControlNet": "해당 ControlNet으로 전환",
         "delete": "삭제",
         "controlAdapter_other": "Control Adapter(s)",

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

@@ -1033,6 +1033,7 @@
         "prompt": "Prompt",
         "depthMidasDescription": "Genereer diepteblad via Midas",
         "controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
+        "openPoseDescription": "Menselijke pose-benadering via Openpose",
         "control": "Controle",
         "resizeMode": "Modus schaling",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ingeschakeld, $t(common.controlNet)s uitgeschakeld",
@@ -1071,6 +1072,7 @@
         "normalBaeDescription": "Normale BAE-verwerking",
         "noneDescription": "Geen verwerking toegepast",
         "saveControlImage": "Bewaar controle-afbeelding",
+        "openPose": "Openpose",
         "toggleControlNet": "Zet deze ControlNet aan/uit",
         "delete": "Verwijder",
         "controlAdapter_one": "Control-adapter",

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

@@ -1155,6 +1155,7 @@
         "resetControlImage": "Сбросить контрольное изображение",
         "prompt": "Запрос",
         "controlnet": "$t(controlnet.controlAdapter_one) №{{number}} $t(common.controlNet)",
+        "openPoseDescription": "Оценка позы человека с помощью Openpose",
         "resizeMode": "Режим изменения размера",
         "t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) включен, $t(common.controlNet)s отключен",
         "weight": "Вес",

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

@@ -259,6 +259,7 @@
         "mediapipeFace": "Mediapipe Yüz",
         "megaControl": "Aşırı Yönetim",
         "mlsd": "M-LSD",
+        "openPoseDescription": "Openpose kullanarak poz belirleme",
         "setControlImageDimensions": "Yönetim Görseli Boyutlarını En/Boydan Al",
         "pidi": "PIDI",
         "scribble": "çiziktirme",
@@ -272,6 +273,7 @@
         "mlsdDescription": "Minimalist Line Segment Detector (Kolay Çizgi Parçası Algılama)",
         "normalBae": "Normal BAE",
         "normalBaeDescription": "Normal BAE işleme",
+        "openPose": "Openpose",
         "resetControlImage": "Yönetim Görselini Kaldır",
         "enableIPAdapter": "IP Aracını Etkinleştir",
         "lineart": "Çizim",

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

@@ -1143,6 +1143,7 @@
         "balanced": "平衡",
         "prompt": "Prompt (提示词控制)",
         "depthMidasDescription": "使用 Midas 生成深度图",
+        "openPoseDescription": "使用 Openpose 进行人体姿态估计",
         "resizeMode": "缩放模式",
         "weight": "权重",
         "selectModel": "选择一个模型",
@@ -1206,6 +1207,7 @@
         "megaControl": "Mega Control (超级控制)",
         "depthZoe": "Depth (Zoe)",
         "colorMap": "Color",
+        "openPose": "Openpose",
         "controlAdapter_other": "Control Adapters",
         "lineartAnime": "Lineart Anime",
         "canny": "Canny",

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

@@ -2,7 +2,7 @@ import type { UnknownAction } from '@reduxjs/toolkit';
 import { isAnyGraphBuilt } from 'features/nodes/store/actions';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
 import { cloneDeep } from 'lodash-es';
-import { appInfoApi } from 'services/api/endpoints/appInfo';
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import type { Graph } from 'services/api/types';
 import { socketGeneratorProgress } from 'services/events/actions';
 
@@ -18,7 +18,7 @@ export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
     }
   }
 
-  if (appInfoApi.endpoints.getOpenAPISchema.matchFulfilled(action)) {
+  if (receivedOpenAPISchema.fulfilled.match(action)) {
     return {
       ...action,
       payload: '<OpenAPI schema omitted>',

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

@@ -23,7 +23,6 @@ import { addControlNetImageProcessedListener } from './listeners/controlNetImage
 import { addEnqueueRequestedCanvasListener } from './listeners/enqueueRequestedCanvas';
 import { addEnqueueRequestedLinear } from './listeners/enqueueRequestedLinear';
 import { addEnqueueRequestedNodes } from './listeners/enqueueRequestedNodes';
-import { addGetOpenAPISchemaListener } from './listeners/getOpenAPISchema';
 import {
   addImageAddedToBoardFulfilledListener,
   addImageAddedToBoardRejectedListener,
@@ -48,6 +47,7 @@ import { addInitialImageSelectedListener } from './listeners/initialImageSelecte
 import { addModelSelectedListener } from './listeners/modelSelected';
 import { addModelsLoadedListener } from './listeners/modelsLoaded';
 import { addDynamicPromptsListener } from './listeners/promptChanged';
+import { addReceivedOpenAPISchemaListener } from './listeners/receivedOpenAPISchema';
 import { addSocketConnectedEventListener as addSocketConnectedListener } from './listeners/socketio/socketConnected';
 import { addSocketDisconnectedEventListener as addSocketDisconnectedListener } from './listeners/socketio/socketDisconnected';
 import { addGeneratorProgressEventListener as addGeneratorProgressListener } from './listeners/socketio/socketGeneratorProgress';
@@ -150,7 +150,7 @@ addImageRemovedFromBoardRejectedListener();
 addBoardIdSelectedListener();
 
 // Node schemas
-addGetOpenAPISchemaListener();
+addReceivedOpenAPISchemaListener();
 
 // Workflows
 addWorkflowLoadRequestedListener();

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

@@ -3,18 +3,18 @@ import { parseify } from 'common/util/serialize';
 import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
 import { size } from 'lodash-es';
-import { appInfoApi } from 'services/api/endpoints/appInfo';
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 
 import { startAppListening } from '..';
 
-export const addGetOpenAPISchemaListener = () => {
+export const addReceivedOpenAPISchemaListener = () => {
   startAppListening({
-    matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
+    actionCreator: receivedOpenAPISchema.fulfilled,
     effect: (action, { dispatch, getState }) => {
       const log = logger('system');
       const schemaJSON = action.payload;
 
-      log.debug({ schemaJSON: parseify(schemaJSON) }, 'Received OpenAPI schema');
+      log.debug({ schemaJSON }, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
@@ -26,14 +26,10 @@ export const addGetOpenAPISchemaListener = () => {
   });
 
   startAppListening({
-    matcher: appInfoApi.endpoints.getOpenAPISchema.matchRejected,
+    actionCreator: receivedOpenAPISchema.rejected,
     effect: (action) => {
-      // If action.meta.condition === true, the request was canceled/skipped because another request was in flight or
-      // the value was already in the cache. We don't want to log these errors.
-      if (!action.meta.condition) {
-        const log = logger('system');
-        log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
-      }
+      const log = logger('system');
+      log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
     },
   });
 };

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

@@ -1,9 +1,10 @@
 import { logger } from 'app/logging/logger';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
-import { isEqual } from 'lodash-es';
+import { isEqual, size } from 'lodash-es';
 import { atom } from 'nanostores';
 import { api } from 'services/api';
 import { queueApi, selectQueueStatus } from 'services/api/endpoints/queue';
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import { socketConnected } from 'services/events/actions';
 
 import { startAppListening } from '../..';
@@ -76,4 +77,17 @@ export const addSocketConnectedEventListener = () => {
       }
     },
   });
+
+  startAppListening({
+    actionCreator: socketConnected,
+    effect: async (action, { dispatch, getState }) => {
+      const { nodeTemplates, config } = getState();
+      // We only want to re-fetch the schema if we don't have any node templates
+      if (!size(nodeTemplates.templates) && !config.disabledTabs.includes('nodes')) {
+        // This request is a createAsyncThunk - resetting API state as in the above listener
+        // will not trigger this request, so we need to manually do it.
+        dispatch(receivedOpenAPISchema());
+      }
+    },
+  });
 };

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

@@ -6,6 +6,7 @@ import { WorkflowMigrationError, WorkflowVersionError } from 'features/nodes/typ
 import { validateWorkflow } from 'features/nodes/util/workflow/validateWorkflow';
 import { addToast } from 'features/system/store/systemSlice';
 import { makeToast } from 'features/system/util/makeToast';
+import { setActiveTab } from 'features/ui/store/uiSlice';
 import { t } from 'i18next';
 import { z } from 'zod';
 import { fromZodError } from 'zod-validation-error';
@@ -52,6 +53,7 @@ export const addWorkflowLoadRequestedListener = () => {
           });
         }
 
+        dispatch(setActiveTab('nodes'));
         requestAnimationFrame(() => {
           $flow.get()?.fitView();
         });

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

@@ -1,28 +1,22 @@
-import { useStore } from '@nanostores/react';
 import { useAppToaster } from 'app/components/Toaster';
-import { $authToken } from 'app/store/nanostores/authToken';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { imageDownloaded } from 'features/gallery/store/actions';
 import { useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 
+import { useImageUrlToBlob } from './useImageUrlToBlob';
+
 export const useDownloadImage = () => {
   const toaster = useAppToaster();
   const { t } = useTranslation();
+  const imageUrlToBlob = useImageUrlToBlob();
   const dispatch = useAppDispatch();
-  const authToken = useStore($authToken);
 
   const downloadImage = useCallback(
     async (image_url: string, image_name: string) => {
       try {
-        const requestOpts = authToken
-          ? {
-              headers: {
-                Authorization: `Bearer ${authToken}`,
-              },
-            }
-          : {};
-        const blob = await fetch(image_url, requestOpts).then((resp) => resp.blob());
+        const blob = await imageUrlToBlob(image_url);
+
         if (!blob) {
           throw new Error('Unable to create Blob');
         }
@@ -46,7 +40,7 @@ export const useDownloadImage = () => {
         });
       }
     },
-    [t, toaster, dispatch, authToken]
+    [t, toaster, imageUrlToBlob, dispatch]
   );
 
   return { downloadImage };

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

@@ -5,7 +5,6 @@ import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAIDndImage from 'common/components/IAIDndImage';
 import IAIDndImageIcon from 'common/components/IAIDndImageIcon';
-import { roundToMultiple } from 'common/util/roundDownToMultiple';
 import { setBoundingBoxDimensions } from 'features/canvas/store/canvasSlice';
 import { useControlAdapterControlImage } from 'features/controlAdapters/hooks/useControlAdapterControlImage';
 import { useControlAdapterProcessedControlImage } from 'features/controlAdapters/hooks/useControlAdapterProcessedControlImage';
@@ -92,14 +91,19 @@ const ControlAdapterImagePreview = ({ isSmall, id }: Props) => {
       return;
     }
 
-    const width = roundToMultiple(controlImage.width, 8);
-    const height = roundToMultiple(controlImage.height, 8);
-
     if (activeTabName === 'unifiedCanvas') {
-      dispatch(setBoundingBoxDimensions({ width, height }, optimalDimension));
+      dispatch(
+        setBoundingBoxDimensions(
+          {
+            width: controlImage.width,
+            height: controlImage.height,
+          },
+          optimalDimension
+        )
+      );
     } else {
-      dispatch(widthChanged(width));
-      dispatch(heightChanged(height));
+      dispatch(widthChanged(controlImage.width));
+      dispatch(heightChanged(controlImage.height));
     }
   }, [controlImage, activeTabName, dispatch, optimalDimension]);
 

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

@@ -6,7 +6,6 @@ import CannyProcessor from './processors/CannyProcessor';
 import ColorMapProcessor from './processors/ColorMapProcessor';
 import ContentShuffleProcessor from './processors/ContentShuffleProcessor';
 import DepthAnyThingProcessor from './processors/DepthAnyThingProcessor';
-import DWOpenposeProcessor from './processors/DWOpenposeProcessor';
 import HedProcessor from './processors/HedProcessor';
 import LineartAnimeProcessor from './processors/LineartAnimeProcessor';
 import LineartProcessor from './processors/LineartProcessor';
@@ -14,6 +13,7 @@ import MediapipeFaceProcessor from './processors/MediapipeFaceProcessor';
 import MidasDepthProcessor from './processors/MidasDepthProcessor';
 import MlsdImageProcessor from './processors/MlsdImageProcessor';
 import NormalBaeProcessor from './processors/NormalBaeProcessor';
+import OpenposeProcessor from './processors/OpenposeProcessor';
 import PidiProcessor from './processors/PidiProcessor';
 import ZoeDepthProcessor from './processors/ZoeDepthProcessor';
 
@@ -73,8 +73,8 @@ const ControlAdapterProcessorComponent = ({ id }: Props) => {
     return <NormalBaeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
   }
 
-  if (processorNode.type === 'dw_openpose_image_processor') {
-    return <DWOpenposeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
+  if (processorNode.type === 'openpose_image_processor') {
+    return <OpenposeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
   }
 
   if (processorNode.type === 'pidi_image_processor') {

invokeai/frontend/web/src/features/controlAdapters/components/processors/DWOpenposeProcessor.tsx

@@ -1,92 +0,0 @@
-import { CompositeNumberInput, CompositeSlider, Flex, FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
-import { useProcessorNodeChanged } from 'features/controlAdapters/components/hooks/useProcessorNodeChanged';
-import { CONTROLNET_PROCESSORS } from 'features/controlAdapters/store/constants';
-import type { RequiredDWOpenposeImageProcessorInvocation } from 'features/controlAdapters/store/types';
-import type { ChangeEvent } from 'react';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-import ProcessorWrapper from './common/ProcessorWrapper';
-
-const DEFAULTS = CONTROLNET_PROCESSORS.dw_openpose_image_processor
-  .default as RequiredDWOpenposeImageProcessorInvocation;
-
-type Props = {
-  controlNetId: string;
-  processorNode: RequiredDWOpenposeImageProcessorInvocation;
-  isEnabled: boolean;
-};
-
-const DWOpenposeProcessor = (props: Props) => {
-  const { controlNetId, processorNode, isEnabled } = props;
-  const { image_resolution, draw_body, draw_face, draw_hands } = processorNode;
-  const processorChanged = useProcessorNodeChanged();
-  const { t } = useTranslation();
-
-  const handleDrawBodyChanged = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => {
-      processorChanged(controlNetId, { draw_body: e.target.checked });
-    },
-    [controlNetId, processorChanged]
-  );
-
-  const handleDrawFaceChanged = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => {
-      processorChanged(controlNetId, { draw_face: e.target.checked });
-    },
-    [controlNetId, processorChanged]
-  );
-
-  const handleDrawHandsChanged = useCallback(
-    (e: ChangeEvent<HTMLInputElement>) => {
-      processorChanged(controlNetId, { draw_hands: e.target.checked });
-    },
-    [controlNetId, processorChanged]
-  );
-
-  const handleImageResolutionChanged = useCallback(
-    (v: number) => {
-      processorChanged(controlNetId, { image_resolution: v });
-    },
-    [controlNetId, processorChanged]
-  );
-
-  return (
-    <ProcessorWrapper>
-      <Flex sx={{ flexDir: 'row', gap: 6 }}>
-        <FormControl isDisabled={!isEnabled} w="max-content">
-          <FormLabel>{t('controlnet.body')}</FormLabel>
-          <Switch defaultChecked={DEFAULTS.draw_body} isChecked={draw_body} onChange={handleDrawBodyChanged} />
-        </FormControl>
-        <FormControl isDisabled={!isEnabled} w="max-content">
-          <FormLabel>{t('controlnet.face')}</FormLabel>
-          <Switch defaultChecked={DEFAULTS.draw_face} isChecked={draw_face} onChange={handleDrawFaceChanged} />
-        </FormControl>
-        <FormControl isDisabled={!isEnabled} w="max-content">
-          <FormLabel>{t('controlnet.hands')}</FormLabel>
-          <Switch defaultChecked={DEFAULTS.draw_hands} isChecked={draw_hands} onChange={handleDrawHandsChanged} />
-        </FormControl>
-      </Flex>
-      <FormControl isDisabled={!isEnabled}>
-        <FormLabel>{t('controlnet.imageResolution')}</FormLabel>
-        <CompositeSlider
-          value={image_resolution}
-          onChange={handleImageResolutionChanged}
-          defaultValue={DEFAULTS.image_resolution}
-          min={0}
-          max={4096}
-          marks
-        />
-        <CompositeNumberInput
-          value={image_resolution}
-          onChange={handleImageResolutionChanged}
-          defaultValue={DEFAULTS.image_resolution}
-          min={0}
-          max={4096}
-        />
-      </FormControl>
-    </ProcessorWrapper>
-  );
-};
-
-export default memo(DWOpenposeProcessor);

invokeai/frontend/web/src/features/controlAdapters/components/processors/OpenposeProcessor.tsx

@@ -0,0 +1,92 @@
+import { CompositeNumberInput, CompositeSlider, FormControl, FormLabel, Switch } from '@invoke-ai/ui-library';
+import { useProcessorNodeChanged } from 'features/controlAdapters/components/hooks/useProcessorNodeChanged';
+import { CONTROLNET_PROCESSORS } from 'features/controlAdapters/store/constants';
+import type { RequiredOpenposeImageProcessorInvocation } from 'features/controlAdapters/store/types';
+import type { ChangeEvent } from 'react';
+import { memo, useCallback } from 'react';
+import { useTranslation } from 'react-i18next';
+
+import ProcessorWrapper from './common/ProcessorWrapper';
+
+const DEFAULTS = CONTROLNET_PROCESSORS.openpose_image_processor.default as RequiredOpenposeImageProcessorInvocation;
+
+type Props = {
+  controlNetId: string;
+  processorNode: RequiredOpenposeImageProcessorInvocation;
+  isEnabled: boolean;
+};
+
+const OpenposeProcessor = (props: Props) => {
+  const { controlNetId, processorNode, isEnabled } = props;
+  const { image_resolution, detect_resolution, hand_and_face } = processorNode;
+  const processorChanged = useProcessorNodeChanged();
+  const { t } = useTranslation();
+
+  const handleDetectResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { detect_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleImageResolutionChanged = useCallback(
+    (v: number) => {
+      processorChanged(controlNetId, { image_resolution: v });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  const handleHandAndFaceChanged = useCallback(
+    (e: ChangeEvent<HTMLInputElement>) => {
+      processorChanged(controlNetId, { hand_and_face: e.target.checked });
+    },
+    [controlNetId, processorChanged]
+  );
+
+  return (
+    <ProcessorWrapper>
+      <FormControl isDisabled={!isEnabled}>
+        <FormLabel>{t('controlnet.detectResolution')}</FormLabel>
+        <CompositeSlider
+          value={detect_resolution}
+          onChange={handleDetectResolutionChanged}
+          defaultValue={DEFAULTS.detect_resolution}
+          min={0}
+          max={4096}
+          marks
+        />
+        <CompositeNumberInput
+          value={detect_resolution}
+          onChange={handleDetectResolutionChanged}
+          defaultValue={DEFAULTS.detect_resolution}
+          min={0}
+          max={4096}
+        />
+      </FormControl>
+      <FormControl isDisabled={!isEnabled}>
+        <FormLabel>{t('controlnet.imageResolution')}</FormLabel>
+        <CompositeSlider
+          value={image_resolution}
+          onChange={handleImageResolutionChanged}
+          defaultValue={DEFAULTS.image_resolution}
+          min={0}
+          max={4096}
+          marks
+        />
+        <CompositeNumberInput
+          value={image_resolution}
+          onChange={handleImageResolutionChanged}
+          defaultValue={DEFAULTS.image_resolution}
+          min={0}
+          max={4096}
+        />
+      </FormControl>
+      <FormControl isDisabled={!isEnabled}>
+        <FormLabel>{t('controlnet.handAndFace')}</FormLabel>
+        <Switch isChecked={hand_and_face} onChange={handleHandAndFaceChanged} />
+      </FormControl>
+    </ProcessorWrapper>
+  );
+};
+
+export default memo(OpenposeProcessor);

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

@@ -205,21 +205,20 @@ export const CONTROLNET_PROCESSORS: ControlNetProcessorsDict = {
       image_resolution: 512,
     },
   },
-  dw_openpose_image_processor: {
-    type: 'dw_openpose_image_processor',
+  openpose_image_processor: {
+    type: 'openpose_image_processor',
     get label() {
-      return i18n.t('controlnet.dwOpenpose');
+      return i18n.t('controlnet.openPose');
     },
     get description() {
-      return i18n.t('controlnet.dwOpenposeDescription');
+      return i18n.t('controlnet.openPoseDescription');
     },
     default: {
-      id: 'dw_openpose_image_processor',
-      type: 'dw_openpose_image_processor',
+      id: 'openpose_image_processor',
+      type: 'openpose_image_processor',
+      detect_resolution: 512,
       image_resolution: 512,
-      draw_body: true,
-      draw_face: false,
-      draw_hands: false,
+      hand_and_face: false,
     },
   },
   pidi_image_processor: {
@@ -267,7 +266,7 @@ export const CONTROLNET_MODEL_DEFAULT_PROCESSORS: {
   lineart_anime: 'lineart_anime_image_processor',
   softedge: 'hed_image_processor',
   shuffle: 'content_shuffle_image_processor',
-  openpose: 'dw_openpose_image_processor',
+  openpose: 'openpose_image_processor',
   mediapipe: 'mediapipe_face_processor',
   pidi: 'pidi_image_processor',
   zoe: 'zoe_depth_image_processor',

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

@@ -11,7 +11,6 @@ import type {
   ColorMapImageProcessorInvocation,
   ContentShuffleImageProcessorInvocation,
   DepthAnythingImageProcessorInvocation,
-  DWOpenposeImageProcessorInvocation,
   HedImageProcessorInvocation,
   LineartAnimeImageProcessorInvocation,
   LineartImageProcessorInvocation,
@@ -19,6 +18,7 @@ import type {
   MidasDepthImageProcessorInvocation,
   MlsdImageProcessorInvocation,
   NormalbaeImageProcessorInvocation,
+  OpenposeImageProcessorInvocation,
   PidiImageProcessorInvocation,
   ZoeDepthImageProcessorInvocation,
 } from 'services/api/types';
@@ -40,7 +40,7 @@ export type ControlAdapterProcessorNode =
   | MidasDepthImageProcessorInvocation
   | MlsdImageProcessorInvocation
   | NormalbaeImageProcessorInvocation
-  | DWOpenposeImageProcessorInvocation
+  | OpenposeImageProcessorInvocation
   | PidiImageProcessorInvocation
   | ZoeDepthImageProcessorInvocation;
 
@@ -143,11 +143,11 @@ export type RequiredNormalbaeImageProcessorInvocation = O.Required<
 >;
 
 /**
- * The DW Openpose processor node, with parameters flagged as required
+ * The Openpose processor node, with parameters flagged as required
  */
-export type RequiredDWOpenposeImageProcessorInvocation = O.Required<
-  DWOpenposeImageProcessorInvocation,
-  'type' | 'image_resolution' | 'draw_body' | 'draw_face' | 'draw_hands'
+export type RequiredOpenposeImageProcessorInvocation = O.Required<
+  OpenposeImageProcessorInvocation,
+  'type' | 'detect_resolution' | 'image_resolution' | 'hand_and_face'
 >;
 
 /**
@@ -179,7 +179,7 @@ export type RequiredControlAdapterProcessorNode =
       | RequiredMidasDepthImageProcessorInvocation
       | RequiredMlsdImageProcessorInvocation
       | RequiredNormalbaeImageProcessorInvocation
-      | RequiredDWOpenposeImageProcessorInvocation
+      | RequiredOpenposeImageProcessorInvocation
       | RequiredPidiImageProcessorInvocation
       | RequiredZoeDepthImageProcessorInvocation,
       'id'
@@ -299,10 +299,10 @@ export const isNormalbaeImageProcessorInvocation = (obj: unknown): obj is Normal
 };
 
 /**
- * Type guard for DWOpenposeImageProcessorInvocation
+ * Type guard for OpenposeImageProcessorInvocation
  */
-export const isDWOpenposeImageProcessorInvocation = (obj: unknown): obj is DWOpenposeImageProcessorInvocation => {
-  if (isObject(obj) && 'type' in obj && obj.type === 'dw_openpose_image_processor') {
+export const isOpenposeImageProcessorInvocation = (obj: unknown): obj is OpenposeImageProcessorInvocation => {
+  if (isObject(obj) && 'type' in obj && obj.type === 'openpose_image_processor') {
     return true;
   }
   return false;

invokeai/frontend/web/src/features/dnd/components/DndSortable.tsx

@@ -1,23 +0,0 @@
-import type { DragEndEvent } from '@dnd-kit/core';
-import { SortableContext, verticalListSortingStrategy } from '@dnd-kit/sortable';
-import type { PropsWithChildren } from 'react';
-import { memo } from 'react';
-
-import { DndContextTypesafe } from './DndContextTypesafe';
-
-type Props = PropsWithChildren & {
-  items: string[];
-  onDragEnd(event: DragEndEvent): void;
-};
-
-const DndSortable = (props: Props) => {
-  return (
-    <DndContextTypesafe onDragEnd={props.onDragEnd}>
-      <SortableContext items={props.items} strategy={verticalListSortingStrategy}>
-        {props.children}
-      </SortableContext>
-    </DndContextTypesafe>
-  );
-};
-
-export default memo(DndSortable);

invokeai/frontend/web/src/features/lora/store/loraSlice.ts

@@ -35,7 +35,7 @@ export const loraSlice = createSlice({
     },
     loraRecalled: (state, action: PayloadAction<LoRAModelConfigEntity & { weight: number }>) => {
       const { model_name, id, base_model, weight } = action.payload;
-      state.loras[id] = { id, model_name, base_model, weight, isEnabled: true };
+      state.loras[id] = { id, model_name, base_model, weight };
     },
     loraRemoved: (state, action: PayloadAction<string>) => {
       const id = action.payload;

invokeai/frontend/web/src/features/nodes/components/NodeEditor.tsx

@@ -1,6 +1,7 @@
 import 'reactflow/dist/style.css';
 
 import { Flex } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import TopPanel from 'features/nodes/components/flow/panels/TopPanel/TopPanel';
 import { SaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog/SaveWorkflowAsDialog';
@@ -10,7 +11,6 @@ import type { CSSProperties } from 'react';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { MdDeviceHub } from 'react-icons/md';
-import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 import AddNodePopover from './flow/AddNodePopover/AddNodePopover';
 import { Flow } from './flow/Flow';
@@ -40,7 +40,7 @@ const exit: AnimationProps['exit'] = {
 };
 
 const NodeEditor = () => {
-  const { data, isLoading } = useGetOpenAPISchemaQuery();
+  const isReady = useAppSelector((s) => s.nodes.isReady);
   const { t } = useTranslation();
   return (
     <Flex
@@ -53,7 +53,7 @@ const NodeEditor = () => {
       justifyContent="center"
     >
       <AnimatePresence>
-        {data && (
+        {isReady && (
           <motion.div initial={initial} animate={animate} exit={exit} style={isReadyMotionStyles}>
             <Flow />
             <AddNodePopover />
@@ -65,7 +65,7 @@ const NodeEditor = () => {
         )}
       </AnimatePresence>
       <AnimatePresence>
-        {isLoading && (
+        {!isReady && (
           <motion.div initial={initial} animate={animate} exit={exit} style={notIsReadyMotionStyles}>
             <Flex
               layerStyle="first"

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

@@ -0,0 +1,84 @@
+import type { ContextMenuProps } from '@invoke-ai/ui-library';
+import { ContextMenu, MenuGroup, MenuItem, MenuList } from '@invoke-ai/ui-library';
+import { createSelector } from '@reduxjs/toolkit';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useFieldInputKind } from 'features/nodes/hooks/useFieldInputKind';
+import { useFieldLabel } from 'features/nodes/hooks/useFieldLabel';
+import { useFieldTemplateTitle } from 'features/nodes/hooks/useFieldTemplateTitle';
+import {
+  selectWorkflowSlice,
+  workflowExposedFieldAdded,
+  workflowExposedFieldRemoved,
+} from 'features/nodes/store/workflowSlice';
+import type { ReactNode } from 'react';
+import { memo, useCallback, useMemo } from 'react';
+import { useTranslation } from 'react-i18next';
+import { PiMinusBold, PiPlusBold } from 'react-icons/pi';
+
+type Props = {
+  nodeId: string;
+  fieldName: string;
+  kind: 'input' | 'output';
+  children: ContextMenuProps<HTMLDivElement>['children'];
+};
+
+const FieldContextMenu = ({ nodeId, fieldName, kind, children }: Props) => {
+  const dispatch = useAppDispatch();
+  const label = useFieldLabel(nodeId, fieldName);
+  const fieldTemplateTitle = useFieldTemplateTitle(nodeId, fieldName, kind);
+  const input = useFieldInputKind(nodeId, fieldName);
+  const { t } = useTranslation();
+
+  const selectIsExposed = useMemo(
+    () =>
+      createSelector(selectWorkflowSlice, (workflow) => {
+        return Boolean(workflow.exposedFields.find((f) => f.nodeId === nodeId && f.fieldName === fieldName));
+      }),
+    [fieldName, nodeId]
+  );
+
+  const mayExpose = useMemo(() => input && ['any', 'direct'].includes(input), [input]);
+
+  const isExposed = useAppSelector(selectIsExposed);
+
+  const handleExposeField = useCallback(() => {
+    dispatch(workflowExposedFieldAdded({ nodeId, fieldName }));
+  }, [dispatch, fieldName, nodeId]);
+
+  const handleUnexposeField = useCallback(() => {
+    dispatch(workflowExposedFieldRemoved({ nodeId, fieldName }));
+  }, [dispatch, fieldName, nodeId]);
+
+  const menuItems = useMemo(() => {
+    const menuItems: ReactNode[] = [];
+    if (mayExpose && !isExposed) {
+      menuItems.push(
+        <MenuItem key={`${nodeId}.${fieldName}.expose-field`} icon={<PiPlusBold />} onClick={handleExposeField}>
+          {t('nodes.addLinearView')}
+        </MenuItem>
+      );
+    }
+    if (mayExpose && isExposed) {
+      menuItems.push(
+        <MenuItem key={`${nodeId}.${fieldName}.unexpose-field`} icon={<PiMinusBold />} onClick={handleUnexposeField}>
+          {t('nodes.removeLinearView')}
+        </MenuItem>
+      );
+    }
+    return menuItems;
+  }, [fieldName, handleExposeField, handleUnexposeField, isExposed, mayExpose, nodeId, t]);
+
+  const renderMenuFunc = useCallback(
+    () =>
+      !menuItems.length ? null : (
+        <MenuList visibility="visible">
+          <MenuGroup title={label || fieldTemplateTitle || t('nodes.unknownField')}>{menuItems}</MenuGroup>
+        </MenuList>
+      ),
+    [fieldTemplateTitle, label, menuItems, t]
+  );
+
+  return <ContextMenu renderMenu={renderMenuFunc}>{children}</ContextMenu>;
+};
+
+export default memo(FieldContextMenu);

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

@@ -1,68 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { useFieldValue } from 'features/nodes/hooks/useFieldValue';
-import {
-  selectWorkflowSlice,
-  workflowExposedFieldAdded,
-  workflowExposedFieldRemoved,
-} from 'features/nodes/store/workflowSlice';
-import { memo, useCallback, useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiMinusBold, PiPlusBold } from 'react-icons/pi';
-
-type Props = {
-  nodeId: string;
-  fieldName: string;
-};
-
-const FieldLinearViewToggle = ({ nodeId, fieldName }: Props) => {
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-  const value = useFieldValue(nodeId, fieldName);
-  const selectIsExposed = useMemo(
-    () =>
-      createSelector(selectWorkflowSlice, (workflow) => {
-        return Boolean(workflow.exposedFields.find((f) => f.nodeId === nodeId && f.fieldName === fieldName));
-      }),
-    [fieldName, nodeId]
-  );
-
-  const isExposed = useAppSelector(selectIsExposed);
-
-  const handleExposeField = useCallback(() => {
-    dispatch(workflowExposedFieldAdded({ nodeId, fieldName, value }));
-  }, [dispatch, fieldName, nodeId, value]);
-
-  const handleUnexposeField = useCallback(() => {
-    dispatch(workflowExposedFieldRemoved({ nodeId, fieldName }));
-  }, [dispatch, fieldName, nodeId]);
-
-  if (!isExposed) {
-    return (
-      <IconButton
-        variant="ghost"
-        tooltip={t('nodes.addLinearView')}
-        aria-label={t('nodes.addLinearView')}
-        icon={<PiPlusBold />}
-        onClick={handleExposeField}
-        pointerEvents="auto"
-        size="xs"
-      />
-    );
-  } else {
-    return (
-      <IconButton
-        variant="ghost"
-        tooltip={t('nodes.removeLinearView')}
-        aria-label={t('nodes.removeLinearView')}
-        icon={<PiMinusBold />}
-        onClick={handleUnexposeField}
-        pointerEvents="auto"
-        size="xs"
-      />
-    );
-  }
-};
-
-export default memo(FieldLinearViewToggle);

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

@@ -4,12 +4,12 @@ import { useDoesInputHaveValue } from 'features/nodes/hooks/useDoesInputHaveValu
 import { useFieldInputInstance } from 'features/nodes/hooks/useFieldInputInstance';
 import { useFieldInputTemplate } from 'features/nodes/hooks/useFieldInputTemplate';
 import type { PropsWithChildren } from 'react';
-import { memo, useCallback, useMemo, useState } from 'react';
+import { memo, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 
 import EditableFieldTitle from './EditableFieldTitle';
+import FieldContextMenu from './FieldContextMenu';
 import FieldHandle from './FieldHandle';
-import FieldLinearViewToggle from './FieldLinearViewToggle';
 import InputFieldRenderer from './InputFieldRenderer';
 
 interface Props {
@@ -22,7 +22,6 @@ const InputField = ({ nodeId, fieldName }: Props) => {
   const fieldTemplate = useFieldInputTemplate(nodeId, fieldName);
   const fieldInstance = useFieldInputInstance(nodeId, fieldName);
   const doesFieldHaveValue = useDoesInputHaveValue(nodeId, fieldName);
-  const [isHovered, setIsHovered] = useState(false);
 
   const { isConnected, isConnectionInProgress, isConnectionStartField, connectionError, shouldDim } =
     useConnectionState({ nodeId, fieldName, kind: 'input' });
@@ -47,14 +46,6 @@ const InputField = ({ nodeId, fieldName }: Props) => {
     return false;
   }, [fieldTemplate, isConnected, doesFieldHaveValue]);
 
-  const onMouseEnter = useCallback(() => {
-    setIsHovered(true);
-  }, []);
-
-  const onMouseLeave = useCallback(() => {
-    setIsHovered(false);
-  }, []);
-
   if (!fieldTemplate || !fieldInstance) {
     return (
       <InputFieldWrapper shouldDim={shouldDim}>
@@ -96,17 +87,19 @@ const InputField = ({ nodeId, fieldName }: Props) => {
   return (
     <InputFieldWrapper shouldDim={shouldDim}>
       <FormControl isInvalid={isMissingInput} isDisabled={isConnected} orientation="vertical" px={2}>
-        <Flex flexDir="column" w="full" gap={1} onMouseEnter={onMouseEnter} onMouseLeave={onMouseLeave}>
-          <Flex>
-            <EditableFieldTitle
-              nodeId={nodeId}
-              fieldName={fieldName}
-              kind="input"
-              isMissingInput={isMissingInput}
-              withTooltip
-            />
-            {isHovered && <FieldLinearViewToggle nodeId={nodeId} fieldName={fieldName} />}
-          </Flex>
+        <Flex flexDir="column" w="full" gap={1}>
+          <FieldContextMenu nodeId={nodeId} fieldName={fieldName} kind="input">
+            {(ref) => (
+              <EditableFieldTitle
+                ref={ref}
+                nodeId={nodeId}
+                fieldName={fieldName}
+                kind="input"
+                isMissingInput={isMissingInput}
+                withTooltip
+              />
+            )}
+          </FieldContextMenu>
           <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
         </Flex>
       </FormControl>

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

@@ -1,15 +1,12 @@
-import { useSortable } from '@dnd-kit/sortable';
-import { CSS } from '@dnd-kit/utilities';
 import { Flex, Icon, IconButton, Spacer, Tooltip } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
 import NodeSelectionOverlay from 'common/components/NodeSelectionOverlay';
-import { useFieldOriginalValue } from 'features/nodes/hooks/useFieldOriginalValue';
 import { useMouseOverNode } from 'features/nodes/hooks/useMouseOverNode';
 import { workflowExposedFieldRemoved } from 'features/nodes/store/workflowSlice';
 import { HANDLE_TOOLTIP_OPEN_DELAY } from 'features/nodes/types/constants';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiArrowCounterClockwiseBold, PiDotsSixVerticalBold, PiInfoBold, PiTrashSimpleBold } from 'react-icons/pi';
+import { PiInfoBold, PiTrashSimpleBold } from 'react-icons/pi';
 
 import EditableFieldTitle from './EditableFieldTitle';
 import FieldTooltipContent from './FieldTooltipContent';
@@ -22,79 +19,46 @@ type Props = {
 
 const LinearViewField = ({ nodeId, fieldName }: Props) => {
   const dispatch = useAppDispatch();
-  const { isValueChanged, onReset } = useFieldOriginalValue(nodeId, fieldName);
   const { isMouseOverNode, handleMouseOut, handleMouseOver } = useMouseOverNode(nodeId);
   const { t } = useTranslation();
-
   const handleRemoveField = useCallback(() => {
     dispatch(workflowExposedFieldRemoved({ nodeId, fieldName }));
   }, [dispatch, fieldName, nodeId]);
 
-  const { attributes, listeners, setNodeRef, transform, transition } = useSortable({ id: `${nodeId}.${fieldName}` });
-
-  const style = {
-    transform: CSS.Translate.toString(transform),
-    transition,
-  };
-
   return (
     <Flex
       onMouseEnter={handleMouseOver}
       onMouseLeave={handleMouseOut}
       layerStyle="second"
-      alignItems="center"
       position="relative"
       borderRadius="base"
       w="full"
       p={4}
-      paddingLeft={0}
-      ref={setNodeRef}
-      style={style}
+      flexDir="column"
     >
-      <IconButton
-        aria-label={t('nodes.reorderLinearView')}
-        variant="ghost"
-        icon={<PiDotsSixVerticalBold />}
-        {...listeners}
-        {...attributes}
-        mx={2}
-        height="full"
-      />
-      <Flex flexDir="column" w="full">
-        <Flex alignItems="center">
-          <EditableFieldTitle nodeId={nodeId} fieldName={fieldName} kind="input" />
-          <Spacer />
-          {isValueChanged && (
-            <IconButton
-              aria-label={t('nodes.resetToDefaultValue')}
-              tooltip={t('nodes.resetToDefaultValue')}
-              variant="ghost"
-              size="sm"
-              onClick={onReset}
-              icon={<PiArrowCounterClockwiseBold />}
-            />
-          )}
-          <Tooltip
-            label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
-            openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
-            placement="top"
-          >
-            <Flex h="full" alignItems="center">
-              <Icon fontSize="sm" color="base.300" as={PiInfoBold} />
-            </Flex>
-          </Tooltip>
-          <IconButton
-            aria-label={t('nodes.removeLinearView')}
-            tooltip={t('nodes.removeLinearView')}
-            variant="ghost"
-            size="sm"
-            onClick={handleRemoveField}
-            icon={<PiTrashSimpleBold />}
-          />
-        </Flex>
-        <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
-        <NodeSelectionOverlay isSelected={false} isHovered={isMouseOverNode} />
+      <Flex>
+        <EditableFieldTitle nodeId={nodeId} fieldName={fieldName} kind="input" />
+        <Spacer />
+        <Tooltip
+          label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
+          openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
+          placement="top"
+        >
+          <Flex h="full" alignItems="center">
+            <Icon fontSize="sm" color="base.300" as={PiInfoBold} />
+          </Flex>
+        </Tooltip>
+        <IconButton
+          aria-label={t('nodes.removeLinearView')}
+          tooltip={t('nodes.removeLinearView')}
+          variant="ghost"
+          size="sm"
+          onClick={handleRemoveField}
+          icon={<PiTrashSimpleBold />}
+        />
       </Flex>
+      <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
+      <NodeSelectionOverlay isSelected={false} isHovered={isMouseOverNode} />
     </Flex>
   );
 };

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

@@ -1,23 +1,25 @@
 import { Flex, Spacer } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
 import AddNodeButton from 'features/nodes/components/flow/panels/TopPanel/AddNodeButton';
 import ClearFlowButton from 'features/nodes/components/flow/panels/TopPanel/ClearFlowButton';
 import SaveWorkflowButton from 'features/nodes/components/flow/panels/TopPanel/SaveWorkflowButton';
 import UpdateNodesButton from 'features/nodes/components/flow/panels/TopPanel/UpdateNodesButton';
-import { WorkflowName } from 'features/nodes/components/sidePanel/WorkflowName';
+import WorkflowName from 'features/nodes/components/flow/panels/TopPanel/WorkflowName';
+import WorkflowLibraryButton from 'features/workflowLibrary/components/WorkflowLibraryButton';
 import WorkflowLibraryMenu from 'features/workflowLibrary/components/WorkflowLibraryMenu/WorkflowLibraryMenu';
 import { memo } from 'react';
 
 const TopCenterPanel = () => {
-  const name = useAppSelector((s) => s.workflow.name);
   return (
     <Flex gap={2} top={2} left={2} right={2} position="absolute" alignItems="flex-start" pointerEvents="none">
-      <Flex gap="2">
-        <AddNodeButton />
+      <Flex flexDir="column" gap="2">
+        <Flex gap="2">
+          <AddNodeButton />
+          <WorkflowLibraryButton />
+        </Flex>
         <UpdateNodesButton />
       </Flex>
       <Spacer />
-      {!!name.length && <WorkflowName />}
+      <WorkflowName />
       <Spacer />
       <ClearFlowButton />
       <SaveWorkflowButton />

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/UpdateNodesButton.tsx

@@ -25,7 +25,6 @@ const UpdateNodesButton = () => {
       icon={<PiWarningBold />}
       onClick={handleClickUpdateNodes}
       pointerEvents="auto"
-      colorScheme="warning"
     />
   );
 };

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/WorkflowName.tsx

@@ -0,0 +1,15 @@
+import { Text } from '@invoke-ai/ui-library';
+import { useAppSelector } from 'app/store/storeHooks';
+import { memo } from 'react';
+
+const TopCenterPanel = () => {
+  const name = useAppSelector((s) => s.workflow.name);
+
+  return (
+    <Text m={2} fontSize="lg" userSelect="none" noOfLines={1} wordBreak="break-all" fontWeight="semibold" opacity={0.8}>
+      {name}
+    </Text>
+  );
+};
+
+export default memo(TopCenterPanel);

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

@@ -1,16 +1,17 @@
 import { Button } from '@invoke-ai/ui-library';
+import { useAppDispatch } from 'app/store/storeHooks';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowsClockwiseBold } from 'react-icons/pi';
-import { useLazyGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 
 const ReloadNodeTemplatesButton = () => {
   const { t } = useTranslation();
-  const [_getOpenAPISchema] = useLazyGetOpenAPISchemaQuery();
+  const dispatch = useAppDispatch();
 
   const handleReloadSchema = useCallback(() => {
-    _getOpenAPISchema();
-  }, [_getOpenAPISchema]);
+    dispatch(receivedOpenAPISchema());
+  }, [dispatch]);
 
   return (
     <Button

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

@@ -0,0 +1,15 @@
+import { Flex } from '@invoke-ai/ui-library';
+import WorkflowLibraryButton from 'features/workflowLibrary/components/WorkflowLibraryButton';
+import WorkflowLibraryMenu from 'features/workflowLibrary/components/WorkflowLibraryMenu/WorkflowLibraryMenu';
+import { memo } from 'react';
+
+const TopRightPanel = () => {
+  return (
+    <Flex gap={2} position="absolute" top={2} insetInlineEnd={2}>
+      <WorkflowLibraryButton />
+      <WorkflowLibraryMenu />
+    </Flex>
+  );
+};
+
+export default memo(TopRightPanel);

invokeai/frontend/web/src/features/nodes/components/sidePanel/ModeToggle.tsx

@@ -1,43 +0,0 @@
-import { Flex, IconButton } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { workflowModeChanged } from 'features/nodes/store/workflowSlice';
-import { useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiEyeBold, PiPencilBold } from 'react-icons/pi';
-
-export const ModeToggle = () => {
-  const dispatch = useAppDispatch();
-  const mode = useAppSelector((s) => s.workflow.mode);
-  const { t } = useTranslation();
-
-  const onClickEdit = useCallback(() => {
-    dispatch(workflowModeChanged('edit'));
-  }, [dispatch]);
-
-  const onClickView = useCallback(() => {
-    dispatch(workflowModeChanged('view'));
-  }, [dispatch]);
-
-  return (
-    <Flex justifyContent="flex-end">
-      {mode === 'view' && (
-        <IconButton
-          aria-label={t('nodes.editMode')}
-          tooltip={t('nodes.editMode')}
-          onClick={onClickEdit}
-          icon={<PiPencilBold />}
-          colorScheme="invokeBlue"
-        />
-      )}
-      {mode === 'edit' && (
-        <IconButton
-          aria-label={t('nodes.viewMode')}
-          tooltip={t('nodes.viewMode')}
-          onClick={onClickView}
-          icon={<PiEyeBold />}
-          colorScheme="invokeBlue"
-        />
-      )}
-    </Flex>
-  );
-};

invokeai/frontend/web/src/features/nodes/components/sidePanel/NodeEditorPanelGroup.tsx

@@ -1,37 +1,22 @@
 import 'reactflow/dist/style.css';
 
 import { Flex } from '@invoke-ai/ui-library';
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { useAppSelector } from 'app/store/storeHooks';
-import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
 import QueueControls from 'features/queue/components/QueueControls';
 import ResizeHandle from 'features/ui/components/tabs/ResizeHandle';
 import { usePanelStorage } from 'features/ui/hooks/usePanelStorage';
-import WorkflowLibraryButton from 'features/workflowLibrary/components/WorkflowLibraryButton';
 import type { CSSProperties } from 'react';
 import { memo, useCallback, useRef } from 'react';
 import type { ImperativePanelGroupHandle } from 'react-resizable-panels';
 import { Panel, PanelGroup } from 'react-resizable-panels';
 
 import InspectorPanel from './inspector/InspectorPanel';
-import { WorkflowViewMode } from './viewMode/WorkflowViewMode';
 import WorkflowPanel from './workflow/WorkflowPanel';
-import { WorkflowMenu } from './WorkflowMenu';
-import { WorkflowName } from './WorkflowName';
 
 const panelGroupStyles: CSSProperties = { height: '100%', width: '100%' };
 
-const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
-  return {
-    mode: workflow.mode,
-  };
-});
-
 const NodeEditorPanelGroup = () => {
-  const { mode } = useAppSelector(selector);
   const panelGroupRef = useRef<ImperativePanelGroupHandle>(null);
   const panelStorage = usePanelStorage();
-
   const handleDoubleClickHandle = useCallback(() => {
     if (!panelGroupRef.current) {
       return;
@@ -42,33 +27,22 @@ const NodeEditorPanelGroup = () => {
   return (
     <Flex w="full" h="full" gap={2} flexDir="column">
       <QueueControls />
-      <Flex w="full" justifyContent="space-between" alignItems="center" gap="4" padding={1}>
-        <Flex justifyContent="space-between" alignItems="center" gap="4">
-          <WorkflowLibraryButton />
-          <WorkflowName />
-        </Flex>
-        <WorkflowMenu />
-      </Flex>
-
-      {mode === 'view' && <WorkflowViewMode />}
-      {mode === 'edit' && (
-        <PanelGroup
-          ref={panelGroupRef}
-          id="workflow-panel-group"
-          autoSaveId="workflow-panel-group"
-          direction="vertical"
-          style={panelGroupStyles}
-          storage={panelStorage}
-        >
-          <Panel id="workflow" collapsible minSize={25}>
-            <WorkflowPanel />
-          </Panel>
-          <ResizeHandle orientation="horizontal" onDoubleClick={handleDoubleClickHandle} />
-          <Panel id="inspector" collapsible minSize={25}>
-            <InspectorPanel />
-          </Panel>
-        </PanelGroup>
-      )}
+      <PanelGroup
+        ref={panelGroupRef}
+        id="workflow-panel-group"
+        autoSaveId="workflow-panel-group"
+        direction="vertical"
+        style={panelGroupStyles}
+        storage={panelStorage}
+      >
+        <Panel id="workflow" collapsible minSize={25}>
+          <WorkflowPanel />
+        </Panel>
+        <ResizeHandle orientation="horizontal" onDoubleClick={handleDoubleClickHandle} />
+        <Panel id="inspector" collapsible minSize={25}>
+          <InspectorPanel />
+        </Panel>
+      </PanelGroup>
     </Flex>
   );
 };

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowMenu.tsx

@@ -1,26 +0,0 @@
-import { Flex } from '@invoke-ai/ui-library';
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { useAppSelector } from 'app/store/storeHooks';
-import SaveWorkflowButton from 'features/nodes/components/flow/panels/TopPanel/SaveWorkflowButton';
-import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
-import { NewWorkflowButton } from 'features/workflowLibrary/components/NewWorkflowButton';
-
-import { ModeToggle } from './ModeToggle';
-
-const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
-  return {
-    mode: workflow.mode,
-  };
-});
-
-export const WorkflowMenu = () => {
-  const { mode } = useAppSelector(selector);
-
-  return (
-    <Flex gap="2" alignItems="center">
-      {mode === 'edit' && <SaveWorkflowButton />}
-      <NewWorkflowButton />
-      <ModeToggle />
-    </Flex>
-  );
-};

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowName.tsx

@@ -1,37 +0,0 @@
-import { Flex, Icon, Text, Tooltip } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { useTranslation } from 'react-i18next';
-import { PiDotOutlineFill } from 'react-icons/pi';
-
-import WorkflowInfoTooltipContent from './viewMode/WorkflowInfoTooltipContent';
-import { WorkflowWarning } from './viewMode/WorkflowWarning';
-
-export const WorkflowName = () => {
-  const { name, isTouched, mode } = useAppSelector((s) => s.workflow);
-  const { t } = useTranslation();
-
-  return (
-    <Flex gap="1" alignItems="center">
-      {name.length ? (
-        <Tooltip label={<WorkflowInfoTooltipContent />} placement="top">
-          <Text fontSize="lg" userSelect="none" noOfLines={1} wordBreak="break-all" fontWeight="semibold">
-            {name}
-          </Text>
-        </Tooltip>
-      ) : (
-        <Text fontSize="lg" fontStyle="italic" fontWeight="semibold">
-          {t('workflows.unnamedWorkflow')}
-        </Text>
-      )}
-
-      {isTouched && mode === 'edit' && (
-        <Tooltip label="Workflow has unsaved changes">
-          <Flex>
-            <Icon as={PiDotOutlineFill} boxSize="20px" sx={{ color: 'invokeYellow.500' }} />
-          </Flex>
-        </Tooltip>
-      )}
-      <WorkflowWarning />
-    </Flex>
-  );
-};

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowField.tsx

@@ -1,53 +0,0 @@
-import { Flex, FormLabel, Icon, IconButton, Spacer, Tooltip } from '@invoke-ai/ui-library';
-import FieldTooltipContent from 'features/nodes/components/flow/nodes/Invocation/fields/FieldTooltipContent';
-import InputFieldRenderer from 'features/nodes/components/flow/nodes/Invocation/fields/InputFieldRenderer';
-import { useFieldLabel } from 'features/nodes/hooks/useFieldLabel';
-import { useFieldOriginalValue } from 'features/nodes/hooks/useFieldOriginalValue';
-import { useFieldTemplateTitle } from 'features/nodes/hooks/useFieldTemplateTitle';
-import { HANDLE_TOOLTIP_OPEN_DELAY } from 'features/nodes/types/constants';
-import { t } from 'i18next';
-import { memo } from 'react';
-import { PiArrowCounterClockwiseBold, PiInfoBold } from 'react-icons/pi';
-
-type Props = {
-  nodeId: string;
-  fieldName: string;
-};
-
-const WorkflowField = ({ nodeId, fieldName }: Props) => {
-  const label = useFieldLabel(nodeId, fieldName);
-  const fieldTemplateTitle = useFieldTemplateTitle(nodeId, fieldName, 'input');
-  const { isValueChanged, onReset } = useFieldOriginalValue(nodeId, fieldName);
-
-  return (
-    <Flex layerStyle="second" position="relative" borderRadius="base" w="full" p={4} gap="2" flexDir="column">
-      <Flex alignItems="center">
-        <FormLabel fontSize="sm">{label || fieldTemplateTitle}</FormLabel>
-
-        <Spacer />
-        {isValueChanged && (
-          <IconButton
-            aria-label={t('nodes.resetToDefaultValue')}
-            tooltip={t('nodes.resetToDefaultValue')}
-            variant="ghost"
-            size="sm"
-            onClick={onReset}
-            icon={<PiArrowCounterClockwiseBold />}
-          />
-        )}
-        <Tooltip
-          label={<FieldTooltipContent nodeId={nodeId} fieldName={fieldName} kind="input" />}
-          openDelay={HANDLE_TOOLTIP_OPEN_DELAY}
-          placement="top"
-        >
-          <Flex h="24px" alignItems="center">
-            <Icon fontSize="md" color="base.300" as={PiInfoBold} />
-          </Flex>
-        </Tooltip>
-      </Flex>
-      <InputFieldRenderer nodeId={nodeId} fieldName={fieldName} />
-    </Flex>
-  );
-};
-
-export default memo(WorkflowField);

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowInfoTooltipContent.tsx

@@ -1,68 +0,0 @@
-import { Box, Flex, Text } from '@invoke-ai/ui-library';
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { useAppSelector } from 'app/store/storeHooks';
-import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
-import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
-
-const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
-  return {
-    name: workflow.name,
-    description: workflow.description,
-    notes: workflow.notes,
-    author: workflow.author,
-    tags: workflow.tags,
-  };
-});
-
-const WorkflowInfoTooltipContent = () => {
-  const { name, description, notes, author, tags } = useAppSelector(selector);
-  const { t } = useTranslation();
-
-  return (
-    <Flex flexDir="column" gap="2">
-      {!!name.length && (
-        <Box>
-          <Text fontWeight="semibold">{t('nodes.workflowName')}</Text>
-          <Text opacity={0.7} fontStyle="oblique 5deg">
-            {name}
-          </Text>
-        </Box>
-      )}
-      {!!author.length && (
-        <Box>
-          <Text fontWeight="semibold">{t('nodes.workflowAuthor')}</Text>
-          <Text opacity={0.7} fontStyle="oblique 5deg">
-            {author}
-          </Text>
-        </Box>
-      )}
-      {!!tags.length && (
-        <Box>
-          <Text fontWeight="semibold">{t('nodes.workflowTags')}</Text>
-          <Text opacity={0.7} fontStyle="oblique 5deg">
-            {tags}
-          </Text>
-        </Box>
-      )}
-      {!!description.length && (
-        <Box>
-          <Text fontWeight="semibold">{t('nodes.workflowDescription')}</Text>
-          <Text opacity={0.7} fontStyle="oblique 5deg">
-            {description}
-          </Text>
-        </Box>
-      )}
-      {!!notes.length && (
-        <Box>
-          <Text fontWeight="semibold">{t('nodes.workflowNotes')}</Text>
-          <Text opacity={0.7} fontStyle="oblique 5deg">
-            {notes}
-          </Text>
-        </Box>
-      )}
-    </Flex>
-  );
-};
-
-export default memo(WorkflowInfoTooltipContent);

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowViewMode.tsx

@@ -1,39 +0,0 @@
-import { Box, Flex } from '@invoke-ai/ui-library';
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { useAppSelector } from 'app/store/storeHooks';
-import { IAINoContentFallback } from 'common/components/IAIImageFallback';
-import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
-import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
-import { t } from 'i18next';
-import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
-
-import WorkflowField from './WorkflowField';
-
-const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => {
-  return {
-    fields: workflow.exposedFields,
-    name: workflow.name,
-  };
-});
-
-export const WorkflowViewMode = () => {
-  const { isLoading } = useGetOpenAPISchemaQuery();
-  const { fields } = useAppSelector(selector);
-  return (
-    <Box position="relative" w="full" h="full">
-      <ScrollableContent>
-        <Flex position="relative" flexDir="column" alignItems="flex-start" p={1} gap={2} h="full" w="full">
-          {isLoading ? (
-            <IAINoContentFallback label={t('nodes.loadingNodes')} icon={null} />
-          ) : fields.length ? (
-            fields.map(({ nodeId, fieldName }) => (
-              <WorkflowField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
-            ))
-          ) : (
-            <IAINoContentFallback label={t('nodes.noFieldsLinearview')} icon={null} />
-          )}
-        </Flex>
-      </ScrollableContent>
-    </Box>
-  );
-};

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowWarning.tsx

@@ -1,21 +0,0 @@
-import { Flex, Icon, Tooltip } from '@invoke-ai/ui-library';
-import { useGetNodesNeedUpdate } from 'features/nodes/hooks/useGetNodesNeedUpdate';
-import { PiWarningBold } from 'react-icons/pi';
-
-import { WorkflowWarningTooltip } from './WorkflowWarningTooltip';
-
-export const WorkflowWarning = () => {
-  const nodesNeedUpdate = useGetNodesNeedUpdate();
-
-  if (!nodesNeedUpdate) {
-    return <></>;
-  }
-
-  return (
-    <Tooltip label={<WorkflowWarningTooltip />}>
-      <Flex h="full" alignItems="center" gap="2">
-        <Icon color="warning.400" as={PiWarningBold} />
-      </Flex>
-    </Tooltip>
-  );
-};

invokeai/frontend/web/src/features/nodes/components/sidePanel/viewMode/WorkflowWarningTooltip.tsx

@@ -1,20 +0,0 @@
-import { Flex, Text } from '@invoke-ai/ui-library';
-import { useTranslation } from 'react-i18next';
-
-export const WorkflowWarningTooltip = () => {
-  const { t } = useTranslation();
-
-  return (
-    <Flex flexDir="column" gap="2">
-      <Flex flexDir="column" gap="2">
-        <Text fontWeight="semibold">{t('toast.loadedWithWarnings')}</Text>
-        <Flex flexDir="column">
-          <Text>{t('common.toResolve')}:</Text>
-          <Text>
-            {t('nodes.editMode')} &gt;&gt; {t('nodes.updateAllNodes')} &gt;&gt; {t('common.save')}
-          </Text>
-        </Flex>
-      </Flex>
-    </Flex>
-  );
-};

invokeai/frontend/web/src/features/nodes/components/sidePanel/workflow/WorkflowLinearTab.tsx

@@ -1,61 +1,31 @@
-import { arrayMove } from '@dnd-kit/sortable';
 import { Box, Flex } from '@invoke-ai/ui-library';
 import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { useAppSelector } from 'app/store/storeHooks';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import ScrollableContent from 'common/components/OverlayScrollbars/ScrollableContent';
-import DndSortable from 'features/dnd/components/DndSortable';
-import type { DragEndEvent } from 'features/dnd/types';
 import LinearViewField from 'features/nodes/components/flow/nodes/Invocation/fields/LinearViewField';
-import { selectWorkflowSlice, workflowExposedFieldsReordered } from 'features/nodes/store/workflowSlice';
-import type { FieldIdentifier } from 'features/nodes/types/field';
-import { memo, useCallback } from 'react';
+import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
+import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 const selector = createMemoizedSelector(selectWorkflowSlice, (workflow) => workflow.exposedFields);
 
 const WorkflowLinearTab = () => {
   const fields = useAppSelector(selector);
-  const { isLoading } = useGetOpenAPISchemaQuery();
   const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-
-  const handleDragEnd = useCallback(
-    (event: DragEndEvent) => {
-      const { active, over } = event;
-      const fieldsStrings = fields.map((field) => `${field.nodeId}.${field.fieldName}`);
-
-      if (over && active.id !== over.id) {
-        const oldIndex = fieldsStrings.indexOf(active.id as string);
-        const newIndex = fieldsStrings.indexOf(over.id as string);
-
-        const newFields = arrayMove(fieldsStrings, oldIndex, newIndex)
-          .map((field) => fields.find((obj) => `${obj.nodeId}.${obj.fieldName}` === field))
-          .filter((field) => field) as FieldIdentifier[];
-
-        dispatch(workflowExposedFieldsReordered(newFields));
-      }
-    },
-    [dispatch, fields]
-  );
 
   return (
     <Box position="relative" w="full" h="full">
       <ScrollableContent>
-        <DndSortable onDragEnd={handleDragEnd} items={fields.map((field) => `${field.nodeId}.${field.fieldName}`)}>
-          <Flex position="relative" flexDir="column" alignItems="flex-start" p={1} gap={2} h="full" w="full">
-            {isLoading ? (
-              <IAINoContentFallback label={t('nodes.loadingNodes')} icon={null} />
-            ) : fields.length ? (
-              fields.map(({ nodeId, fieldName }) => (
-                <LinearViewField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
-              ))
-            ) : (
-              <IAINoContentFallback label={t('nodes.noFieldsLinearview')} icon={null} />
-            )}
-          </Flex>
-        </DndSortable>
+        <Flex position="relative" flexDir="column" alignItems="flex-start" p={1} gap={2} h="full" w="full">
+          {fields.length ? (
+            fields.map(({ nodeId, fieldName }) => (
+              <LinearViewField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
+            ))
+          ) : (
+            <IAINoContentFallback label={t('nodes.noFieldsLinearview')} icon={null} />
+          )}
+        </Flex>
       </ScrollableContent>
     </Box>
   );

invokeai/frontend/web/src/features/nodes/components/sidePanel/workflow/WorkflowPanel.tsx

@@ -12,17 +12,17 @@ const WorkflowPanel = () => {
     <Flex layerStyle="first" flexDir="column" w="full" h="full" borderRadius="base" p={2} gap={2}>
       <Tabs variant="line" display="flex" w="full" h="full" flexDir="column">
         <TabList>
-          <Tab>{t('common.details')}</Tab>
           <Tab>{t('common.linear')}</Tab>
+          <Tab>{t('common.details')}</Tab>
           <Tab>JSON</Tab>
         </TabList>
 
         <TabPanels>
           <TabPanel>
-            <WorkflowGeneralTab />
+            <WorkflowLinearTab />
           </TabPanel>
           <TabPanel>
-            <WorkflowLinearTab />
+            <WorkflowGeneralTab />
           </TabPanel>
           <TabPanel>
             <WorkflowJSONTab />

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

@@ -1,28 +0,0 @@
-import { createSelector } from '@reduxjs/toolkit';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { useFieldValue } from 'features/nodes/hooks/useFieldValue';
-import { fieldValueReset } from 'features/nodes/store/nodesSlice';
-import { selectWorkflowSlice } from 'features/nodes/store/workflowSlice';
-import { isEqual } from 'lodash-es';
-import { useCallback, useMemo } from 'react';
-
-export const useFieldOriginalValue = (nodeId: string, fieldName: string) => {
-  const dispatch = useAppDispatch();
-  const selectOriginalExposedFieldValues = useMemo(
-    () =>
-      createSelector(
-        selectWorkflowSlice,
-        (workflow) =>
-          workflow.originalExposedFieldValues.find((v) => v.nodeId === nodeId && v.fieldName === fieldName)?.value
-      ),
-    [nodeId, fieldName]
-  );
-  const originalValue = useAppSelector(selectOriginalExposedFieldValues);
-  const value = useFieldValue(nodeId, fieldName);
-  const isValueChanged = useMemo(() => !isEqual(value, originalValue), [value, originalValue]);
-  const onReset = useCallback(() => {
-    dispatch(fieldValueReset({ nodeId, fieldName, value: originalValue }));
-  }, [dispatch, fieldName, nodeId, originalValue]);
-
-  return { originalValue, isValueChanged, onReset };
-};

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

@@ -1,23 +0,0 @@
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { useAppSelector } from 'app/store/storeHooks';
-import { selectNodesSlice } from 'features/nodes/store/nodesSlice';
-import { isInvocationNode } from 'features/nodes/types/invocation';
-import { useMemo } from 'react';
-
-export const useFieldValue = (nodeId: string, fieldName: string) => {
-  const selector = useMemo(
-    () =>
-      createMemoizedSelector(selectNodesSlice, (nodes) => {
-        const node = nodes.nodes.find((node) => node.id === nodeId);
-        if (!isInvocationNode(node)) {
-          return;
-        }
-        return node?.data.inputs[fieldName]?.value;
-      }),
-    [fieldName, nodeId]
-  );
-
-  const value = useAppSelector(selector);
-
-  return value;
-};

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

@@ -2,6 +2,7 @@ import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice, isAnyOf } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
 import { workflowLoaded } from 'features/nodes/store/actions';
+import { nodeTemplatesBuilt } from 'features/nodes/store/nodeTemplatesSlice';
 import { SHARED_NODE_PROPERTIES } from 'features/nodes/types/constants';
 import type {
   BoardFieldValue,
@@ -18,7 +19,6 @@ import type {
   MainModelFieldValue,
   SchedulerFieldValue,
   SDXLRefinerModelFieldValue,
-  StatefulFieldValue,
   StringFieldValue,
   T2IAdapterModelFieldValue,
   VAEModelFieldValue,
@@ -37,7 +37,6 @@ import {
   zMainModelFieldValue,
   zSchedulerFieldValue,
   zSDXLRefinerModelFieldValue,
-  zStatefulFieldValue,
   zStringFieldValue,
   zT2IAdapterModelFieldValue,
   zVAEModelFieldValue,
@@ -66,6 +65,7 @@ import {
   SelectionMode,
   updateEdge,
 } from 'reactflow';
+import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import {
   socketGeneratorProgress,
   socketInvocationComplete,
@@ -92,6 +92,7 @@ export const initialNodesState: NodesState = {
   _version: 1,
   nodes: [],
   edges: [],
+  isReady: false,
   connectionStartParams: null,
   connectionStartFieldType: null,
   connectionMade: false,
@@ -480,9 +481,6 @@ export const nodesSlice = createSlice({
     selectedEdgesChanged: (state, action: PayloadAction<string[]>) => {
       state.selectedEdges = action.payload;
     },
-    fieldValueReset: (state, action: FieldValueAction<StatefulFieldValue>) => {
-      fieldValueReducer(state, action, zStatefulFieldValue);
-    },
     fieldStringValueChanged: (state, action: FieldValueAction<StringFieldValue>) => {
       fieldValueReducer(state, action, zStringFieldValue);
     },
@@ -679,6 +677,10 @@ export const nodesSlice = createSlice({
     },
   },
   extraReducers: (builder) => {
+    builder.addCase(receivedOpenAPISchema.pending, (state) => {
+      state.isReady = false;
+    });
+
     builder.addCase(workflowLoaded, (state, action) => {
       const { nodes, edges } = action.payload;
       state.nodes = applyNodeChanges(
@@ -750,6 +752,9 @@ export const nodesSlice = createSlice({
         });
       }
     });
+    builder.addCase(nodeTemplatesBuilt, (state) => {
+      state.isReady = true;
+    });
   },
 });
 
@@ -765,7 +770,6 @@ export const {
   edgesChanged,
   edgesDeleted,
   edgeUpdated,
-  fieldValueReset,
   fieldBoardValueChanged,
   fieldBooleanValueChanged,
   fieldColorValueChanged,
@@ -840,6 +844,7 @@ export const isAnyNodeOrEdgeMutation = isAnyOf(
   nodeIsOpenChanged,
   nodeLabelChanged,
   nodeNotesChanged,
+  nodesChanged,
   nodesDeleted,
   nodeUseCacheChanged,
   notesNodeValueChanged,
@@ -866,6 +871,7 @@ export const nodesPersistConfig: PersistConfig<NodesState> = {
     'connectionStartFieldType',
     'selectedNodes',
     'selectedEdges',
+    'isReady',
     'nodesToCopy',
     'edgesToCopy',
     'connectionMade',

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

@@ -1,4 +1,4 @@
-import type { FieldIdentifier, FieldType, StatefulFieldValue } from 'features/nodes/types/field';
+import type { FieldType } from 'features/nodes/types/field';
 import type {
   AnyNode,
   InvocationNodeEdge,
@@ -26,6 +26,7 @@ export type NodesState = {
   selectedEdges: string[];
   nodeExecutionStates: Record<string, NodeExecutionState>;
   viewport: Viewport;
+  isReady: boolean;
   nodesToCopy: AnyNode[];
   edgesToCopy: InvocationNodeEdge[];
   isAddNodePopoverOpen: boolean;
@@ -33,16 +34,9 @@ export type NodesState = {
   selectionMode: SelectionMode;
 };
 
-export type WorkflowMode = 'edit' | 'view';
-export type FieldIdentifierWithValue = FieldIdentifier & {
-  value: StatefulFieldValue;
-};
-
 export type WorkflowsState = Omit<WorkflowV2, 'nodes' | 'edges'> & {
   _version: 1;
   isTouched: boolean;
-  mode: WorkflowMode;
-  originalExposedFieldValues: FieldIdentifierWithValue[];
 };
 
 export type NodeTemplatesState = {

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

@@ -2,16 +2,11 @@ import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { PersistConfig, RootState } from 'app/store/store';
 import { workflowLoaded } from 'features/nodes/store/actions';
-import { isAnyNodeOrEdgeMutation, nodeEditorReset, nodesChanged, nodesDeleted } from 'features/nodes/store/nodesSlice';
-import type {
-  FieldIdentifierWithValue,
-  WorkflowMode,
-  WorkflowsState as WorkflowState,
-} from 'features/nodes/store/types';
+import { isAnyNodeOrEdgeMutation, nodeEditorReset, nodesDeleted } from 'features/nodes/store/nodesSlice';
+import type { WorkflowsState as WorkflowState } from 'features/nodes/store/types';
 import type { FieldIdentifier } from 'features/nodes/types/field';
-import { isInvocationNode } from 'features/nodes/types/invocation';
 import type { WorkflowCategory, WorkflowV2 } from 'features/nodes/types/workflow';
-import { cloneDeep, isEqual, omit, uniqBy } from 'lodash-es';
+import { cloneDeep, isEqual, uniqBy } from 'lodash-es';
 
 export const blankWorkflow: Omit<WorkflowV2, 'nodes' | 'edges'> = {
   name: '',
@@ -28,9 +23,7 @@ export const blankWorkflow: Omit<WorkflowV2, 'nodes' | 'edges'> = {
 
 export const initialWorkflowState: WorkflowState = {
   _version: 1,
-  isTouched: false,
-  mode: 'view',
-  originalExposedFieldValues: [],
+  isTouched: true,
   ...blankWorkflow,
 };
 
@@ -38,29 +31,15 @@ export const workflowSlice = createSlice({
   name: 'workflow',
   initialState: initialWorkflowState,
   reducers: {
-    workflowModeChanged: (state, action: PayloadAction<WorkflowMode>) => {
-      state.mode = action.payload;
-    },
-    workflowExposedFieldAdded: (state, action: PayloadAction<FieldIdentifierWithValue>) => {
+    workflowExposedFieldAdded: (state, action: PayloadAction<FieldIdentifier>) => {
       state.exposedFields = uniqBy(
-        state.exposedFields.concat(omit(action.payload, 'value')),
-        (field) => `${field.nodeId}-${field.fieldName}`
-      );
-      state.originalExposedFieldValues = uniqBy(
-        state.originalExposedFieldValues.concat(action.payload),
+        state.exposedFields.concat(action.payload),
         (field) => `${field.nodeId}-${field.fieldName}`
       );
       state.isTouched = true;
     },
     workflowExposedFieldRemoved: (state, action: PayloadAction<FieldIdentifier>) => {
       state.exposedFields = state.exposedFields.filter((field) => !isEqual(field, action.payload));
-      state.originalExposedFieldValues = state.originalExposedFieldValues.filter(
-        (field) => !isEqual(omit(field, 'value'), action.payload)
-      );
-      state.isTouched = true;
-    },
-    workflowExposedFieldsReordered: (state, action: PayloadAction<FieldIdentifier[]>) => {
-      state.exposedFields = action.payload;
       state.isTouched = true;
     },
     workflowNameChanged: (state, action: PayloadAction<string>) => {
@@ -99,43 +78,15 @@ export const workflowSlice = createSlice({
     workflowIDChanged: (state, action: PayloadAction<string>) => {
       state.id = action.payload;
     },
+    workflowReset: () => cloneDeep(initialWorkflowState),
     workflowSaved: (state) => {
       state.isTouched = false;
     },
   },
   extraReducers: (builder) => {
     builder.addCase(workflowLoaded, (state, action) => {
-      const { nodes, edges: _edges, ...workflowExtra } = action.payload;
-
-      const originalExposedFieldValues: FieldIdentifierWithValue[] = [];
-
-      workflowExtra.exposedFields.forEach((field) => {
-        const node = nodes.find((n) => n.id === field.nodeId);
-
-        if (!isInvocationNode(node)) {
-          return;
-        }
-
-        const input = node.data.inputs[field.fieldName];
-
-        if (!input) {
-          return;
-        }
-
-        const originalExposedFieldValue = {
-          nodeId: field.nodeId,
-          fieldName: field.fieldName,
-          value: input.value,
-        };
-        originalExposedFieldValues.push(originalExposedFieldValue);
-      });
-
-      return {
-        ...cloneDeep(initialWorkflowState),
-        ...cloneDeep(workflowExtra),
-        originalExposedFieldValues,
-        mode: state.mode,
-      };
+      const { nodes: _nodes, edges: _edges, ...workflowExtra } = action.payload;
+      return { ...initialWorkflowState, ...cloneDeep(workflowExtra) };
     });
 
     builder.addCase(nodesDeleted, (state, action) => {
@@ -146,29 +97,6 @@ export const workflowSlice = createSlice({
 
     builder.addCase(nodeEditorReset, () => cloneDeep(initialWorkflowState));
 
-    builder.addCase(nodesChanged, (state, action) => {
-      // Not all changes to nodes should result in the workflow being marked touched
-      const filteredChanges = action.payload.filter((change) => {
-        // We always want to mark the workflow as touched if a node is added, removed, or reset
-        if (['add', 'remove', 'reset'].includes(change.type)) {
-          return true;
-        }
-
-        // Position changes can change the position and the dragging status of the node - ignore if the change doesn't
-        // affect the position
-        if (change.type === 'position' && (change.position || change.positionAbsolute)) {
-          return true;
-        }
-
-        // This change isn't relevant
-        return false;
-      });
-
-      if (filteredChanges.length > 0) {
-        state.isTouched = true;
-      }
-    });
-
     builder.addMatcher(isAnyNodeOrEdgeMutation, (state) => {
       state.isTouched = true;
     });
@@ -176,10 +104,8 @@ export const workflowSlice = createSlice({
 });
 
 export const {
-  workflowModeChanged,
   workflowExposedFieldAdded,
   workflowExposedFieldRemoved,
-  workflowExposedFieldsReordered,
   workflowNameChanged,
   workflowCategoryChanged,
   workflowDescriptionChanged,
@@ -189,6 +115,7 @@ export const {
   workflowVersionChanged,
   workflowContactChanged,
   workflowIDChanged,
+  workflowReset,
   workflowSaved,
 } = workflowSlice.actions;
 

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

@@ -23,7 +23,6 @@ import {
   NOISE,
   NOISE_HRF,
   RESIZE_HRF,
-  SEAMLESS,
   VAE_LOADER,
 } from './constants';
 import { setMetadataReceivingNode, upsertMetadata } from './metadata';
@@ -31,6 +30,7 @@ import { setMetadataReceivingNode, upsertMetadata } from './metadata';
 // Copy certain connections from previous DENOISE_LATENTS to new DENOISE_LATENTS_HRF.
 function copyConnectionsToDenoiseLatentsHrf(graph: NonNullableGraph): void {
   const destinationFields = [
+    'vae',
     'control',
     'ip_adapter',
     'metadata',
@@ -107,10 +107,9 @@ export const addHrfToGraph = (state: RootState, graph: NonNullableGraph): void =
   }
   const log = logger('txt2img');
 
-  const { vae, seamlessXAxis, seamlessYAxis } = state.generation;
+  const { vae } = state.generation;
   const { hrfStrength, hrfEnabled, hrfMethod } = state.hrf;
   const isAutoVae = !vae;
-  const isSeamlessEnabled = seamlessXAxis || seamlessYAxis;
   const width = state.generation.width;
   const height = state.generation.height;
   const optimalDimension = selectOptimalDimension(state);
@@ -159,7 +158,7 @@ export const addHrfToGraph = (state: RootState, graph: NonNullableGraph): void =
     },
     {
       source: {
-        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -260,7 +259,7 @@ export const addHrfToGraph = (state: RootState, graph: NonNullableGraph): void =
   graph.edges.push(
     {
       source: {
-        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -323,7 +322,7 @@ export const addHrfToGraph = (state: RootState, graph: NonNullableGraph): void =
   graph.edges.push(
     {
       source: {
-        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
         field: 'vae',
       },
       destination: {

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

@@ -1,7 +1,7 @@
 import type { RootState } from 'app/store/store';
 import type { LoRAMetadataItem } from 'features/nodes/types/metadata';
 import { zLoRAMetadataItem } from 'features/nodes/types/metadata';
-import { filter, size } from 'lodash-es';
+import { forEach, size } from 'lodash-es';
 import type { NonNullableGraph, SDXLLoraLoaderInvocation } from 'services/api/types';
 
 import {
@@ -31,8 +31,8 @@ export const addSDXLLoRAsToGraph = (
    * So we need to inject a LoRA chain into the graph.
    */
 
-  const enabledLoRAs = filter(state.lora.loras, (l) => l.isEnabled ?? false);
-  const loraCount = size(enabledLoRAs);
+  const { loras } = state.lora;
+  const loraCount = size(loras);
 
   if (loraCount === 0) {
     return;
@@ -59,7 +59,7 @@ export const addSDXLLoRAsToGraph = (
   let lastLoraNodeId = '';
   let currentLoraIndex = 0;
 
-  enabledLoRAs.forEach((lora) => {
+  forEach(loras, (lora) => {
     const { model_name, base_model, weight } = lora;
     const currentLoraNodeId = `${LORA_LOADER}_${model_name.replace('.', '_')}`;
 

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

@@ -14,7 +14,6 @@ import {
   SDXL_IMAGE_TO_IMAGE_GRAPH,
   SDXL_TEXT_TO_IMAGE_GRAPH,
   SEAMLESS,
-  VAE_LOADER,
 } from './constants';
 import { upsertMetadata } from './metadata';
 
@@ -24,8 +23,7 @@ export const addSeamlessToLinearGraph = (
   modelLoaderNodeId: string
 ): void => {
   // Remove Existing UNet Connections
-  const { seamlessXAxis, seamlessYAxis, vae } = state.generation;
-  const isAutoVae = !vae;
+  const { seamlessXAxis, seamlessYAxis } = state.generation;
 
   graph.nodes[SEAMLESS] = {
     id: SEAMLESS,
@@ -34,15 +32,6 @@ export const addSeamlessToLinearGraph = (
     seamless_y: seamlessYAxis,
   } as SeamlessModeInvocation;
 
-  if (!isAutoVae) {
-    graph.nodes[VAE_LOADER] = {
-      type: 'vae_loader',
-      id: VAE_LOADER,
-      is_intermediate: true,
-      vae_model: vae,
-    };
-  }
-
   if (seamlessXAxis) {
     upsertMetadata(graph, {
       seamless_x: seamlessXAxis,
@@ -86,7 +75,7 @@ export const addSeamlessToLinearGraph = (
     },
     {
       source: {
-        node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+        node_id: modelLoaderNodeId,
         field: 'vae',
       },
       destination: {

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

@@ -21,7 +21,6 @@ import {
   SDXL_IMAGE_TO_IMAGE_GRAPH,
   SDXL_REFINER_INPAINT_CREATE_MASK,
   SDXL_TEXT_TO_IMAGE_GRAPH,
-  SEAMLESS,
   TEXT_TO_IMAGE_GRAPH,
   VAE_LOADER,
 } from './constants';
@@ -32,16 +31,15 @@ export const addVAEToGraph = (
   graph: NonNullableGraph,
   modelLoaderNodeId: string = MAIN_MODEL_LOADER
 ): void => {
-  const { vae, canvasCoherenceMode, seamlessXAxis, seamlessYAxis } = state.generation;
+  const { vae, canvasCoherenceMode } = state.generation;
   const { boundingBoxScaleMethod } = state.canvas;
   const { refinerModel } = state.sdxl;
 
   const isUsingScaledDimensions = ['auto', 'manual'].includes(boundingBoxScaleMethod);
 
   const isAutoVae = !vae;
-  const isSeamlessEnabled = seamlessXAxis || seamlessYAxis;
 
-  if (!isAutoVae && !isSeamlessEnabled) {
+  if (!isAutoVae) {
     graph.nodes[VAE_LOADER] = {
       type: 'vae_loader',
       id: VAE_LOADER,
@@ -58,7 +56,7 @@ export const addVAEToGraph = (
   ) {
     graph.edges.push({
       source: {
-        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+        node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -76,7 +74,7 @@ export const addVAEToGraph = (
   ) {
     graph.edges.push({
       source: {
-        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+        node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -94,7 +92,7 @@ export const addVAEToGraph = (
   ) {
     graph.edges.push({
       source: {
-        node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+        node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -113,7 +111,7 @@ export const addVAEToGraph = (
     graph.edges.push(
       {
         source: {
-          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {
@@ -123,7 +121,7 @@ export const addVAEToGraph = (
       },
       {
         source: {
-          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {
@@ -133,7 +131,7 @@ export const addVAEToGraph = (
       },
       {
         source: {
-          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {
@@ -147,7 +145,7 @@ export const addVAEToGraph = (
     if (canvasCoherenceMode !== 'unmasked') {
       graph.edges.push({
         source: {
-          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {
@@ -162,7 +160,7 @@ export const addVAEToGraph = (
     if (graph.id === SDXL_CANVAS_INPAINT_GRAPH || graph.id === SDXL_CANVAS_OUTPAINT_GRAPH) {
       graph.edges.push({
         source: {
-          node_id: isSeamlessEnabled ? SEAMLESS : isAutoVae ? modelLoaderNodeId : VAE_LOADER,
+          node_id: isAutoVae ? modelLoaderNodeId : VAE_LOADER,
           field: 'vae',
         },
         destination: {

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

@@ -123,7 +123,6 @@ export const buildCanvasImageToImageGraph = (state: RootState, initialImage: Ima
         id: DENOISE_LATENTS,
         is_intermediate,
         cfg_scale,
-        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: 1 - strength,

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

@@ -58,7 +58,6 @@ export const buildCanvasInpaintGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
-    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     steps,
     img2imgStrength: strength,
@@ -153,7 +152,6 @@ export const buildCanvasInpaintGraph = (
         is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
-        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - strength,
         denoising_end: 1,
@@ -177,7 +175,6 @@ export const buildCanvasInpaintGraph = (
         is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
-        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - canvasCoherenceStrength,
         denoising_end: 1,

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

@@ -60,7 +60,6 @@ export const buildCanvasOutpaintGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
-    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     steps,
     img2imgStrength: strength,
@@ -162,7 +161,6 @@ export const buildCanvasOutpaintGraph = (
         is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
-        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - strength,
         denoising_end: 1,
@@ -186,7 +184,6 @@ export const buildCanvasOutpaintGraph = (
         is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
-        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - canvasCoherenceStrength,
         denoising_end: 1,

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

@@ -124,7 +124,6 @@ export const buildCanvasSDXLImageToImageGraph = (state: RootState, initialImage:
         id: SDXL_DENOISE_LATENTS,
         is_intermediate,
         cfg_scale,
-        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: refinerModel ? Math.min(refinerStart, 1 - strength) : 1 - strength,

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

@@ -60,7 +60,6 @@ export const buildCanvasSDXLInpaintGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
-    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     steps,
     seed,
@@ -152,7 +151,6 @@ export const buildCanvasSDXLInpaintGraph = (
         is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
-        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: refinerModel ? Math.min(refinerStart, 1 - strength) : 1 - strength,
         denoising_end: refinerModel ? refinerStart : 1,
@@ -176,7 +174,6 @@ export const buildCanvasSDXLInpaintGraph = (
         is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
-        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - canvasCoherenceStrength,
         denoising_end: 1,

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

@@ -62,7 +62,6 @@ export const buildCanvasSDXLOutpaintGraph = (
     negativePrompt,
     model,
     cfgScale: cfg_scale,
-    cfgRescaleMultiplier: cfg_rescale_multiplier,
     scheduler,
     steps,
     seed,
@@ -161,7 +160,6 @@ export const buildCanvasSDXLOutpaintGraph = (
         is_intermediate,
         steps: steps,
         cfg_scale: cfg_scale,
-        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: refinerModel ? Math.min(refinerStart, 1 - strength) : 1 - strength,
         denoising_end: refinerModel ? refinerStart : 1,
@@ -185,7 +183,6 @@ export const buildCanvasSDXLOutpaintGraph = (
         is_intermediate,
         steps: canvasCoherenceSteps,
         cfg_scale: cfg_scale,
-        cfg_rescale_multiplier,
         scheduler: scheduler,
         denoising_start: 1 - canvasCoherenceStrength,
         denoising_end: 1,

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

@@ -117,7 +117,6 @@ export const buildCanvasSDXLTextToImageGraph = (state: RootState): NonNullableGr
         id: SDXL_DENOISE_LATENTS,
         is_intermediate,
         cfg_scale,
-        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: 0,

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

@@ -115,7 +115,6 @@ export const buildCanvasTextToImageGraph = (state: RootState): NonNullableGraph
         id: DENOISE_LATENTS,
         is_intermediate,
         cfg_scale,
-        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: 0,

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

@@ -123,7 +123,6 @@ export const buildLinearImageToImageGraph = (state: RootState): NonNullableGraph
         type: 'denoise_latents',
         id: DENOISE_LATENTS,
         cfg_scale,
-        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: 1 - strength,

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

@@ -126,7 +126,6 @@ export const buildLinearSDXLImageToImageGraph = (state: RootState): NonNullableG
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
         cfg_scale,
-        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: refinerModel ? Math.min(refinerStart, 1 - strength) : 1 - strength,

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

@@ -109,7 +109,6 @@ export const buildLinearSDXLTextToImageGraph = (state: RootState): NonNullableGr
         type: 'denoise_latents',
         id: SDXL_DENOISE_LATENTS,
         cfg_scale,
-        cfg_rescale_multiplier,
         scheduler,
         steps,
         denoising_start: 0,

invokeai/frontend/web/src/features/settingsAccordions/components/GenerationSettingsAccordion/GenerationSettingsAccordion.tsx

@@ -23,7 +23,7 @@ import ParamMainModelSelect from 'features/parameters/components/MainModel/Param
 import { selectGenerationSlice } from 'features/parameters/store/generationSlice';
 import { useExpanderToggle } from 'features/settingsAccordions/hooks/useExpanderToggle';
 import { useStandaloneAccordionToggle } from 'features/settingsAccordions/hooks/useStandaloneAccordionToggle';
-import { filter } from 'lodash-es';
+import { filter, size } from 'lodash-es';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 
@@ -33,7 +33,7 @@ const formLabelProps: FormLabelProps = {
 
 const badgesSelector = createMemoizedSelector(selectLoraSlice, selectGenerationSlice, (lora, generation) => {
   const enabledLoRAsCount = filter(lora.loras, (l) => !!l.isEnabled).length;
-  const loraTabBadges = enabledLoRAsCount ? [enabledLoRAsCount] : [];
+  const loraTabBadges = size(lora.loras) ? [enabledLoRAsCount] : [];
   const accordionBadges: (string | number)[] = [];
   if (generation.model) {
     accordionBadges.push(generation.model.model_name);

invokeai/frontend/web/src/features/system/components/SettingsModal/SettingsModal.tsx

@@ -101,11 +101,9 @@ const SettingsModal = ({ children, config }: SettingsModalProps) => {
   const clearStorage = useClearStorage();
 
   const handleOpenSettingsModel = useCallback(() => {
-    if (shouldShowClearIntermediates) {
-      refetchIntermediatesCount();
-    }
+    refetchIntermediatesCount();
     _onSettingsModalOpen();
-  }, [_onSettingsModalOpen, refetchIntermediatesCount, shouldShowClearIntermediates]);
+  }, [_onSettingsModalOpen, refetchIntermediatesCount]);
 
   const handleClickResetWebUI = useCallback(() => {
     clearStorage();

invokeai/frontend/web/src/features/ui/components/tabs/NodesTab.tsx

@@ -1,28 +1,13 @@
-import { Box, Flex } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import CurrentImageDisplay from 'features/gallery/components/CurrentImage/CurrentImageDisplay';
 import NodeEditor from 'features/nodes/components/NodeEditor';
 import { memo } from 'react';
 import { ReactFlowProvider } from 'reactflow';
 
 const NodesTab = () => {
-  const mode = useAppSelector((s) => s.workflow.mode);
-
-  if (mode === 'edit') {
-    return (
-      <ReactFlowProvider>
-        <NodeEditor />
-      </ReactFlowProvider>
-    );
-  } else {
-    return (
-      <Box layerStyle="first" position="relative" w="full" h="full" p={2} borderRadius="base">
-        <Flex w="full" h="full">
-          <CurrentImageDisplay />
-        </Flex>
-      </Box>
-    );
-  }
+  return (
+    <ReactFlowProvider>
+      <NodeEditor />
+    </ReactFlowProvider>
+  );
 };
 
 export default memo(NodesTab);

invokeai/frontend/web/src/features/workflowLibrary/components/NewWorkflowButton.tsx

@@ -1,26 +0,0 @@
-import { IconButton } from '@invoke-ai/ui-library';
-import { NewWorkflowConfirmationAlertDialog } from 'features/workflowLibrary/components/NewWorkflowConfirmationAlertDialog';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-import { PiFilePlusBold } from 'react-icons/pi';
-
-export const NewWorkflowButton = memo(() => {
-  const { t } = useTranslation();
-
-  const renderButton = useCallback(
-    (onClick: () => void) => (
-      <IconButton
-        aria-label={t('nodes.newWorkflow')}
-        tooltip={t('nodes.newWorkflow')}
-        icon={<PiFilePlusBold />}
-        onClick={onClick}
-        pointerEvents="auto"
-      />
-    ),
-    [t]
-  );
-
-  return <NewWorkflowConfirmationAlertDialog renderButton={renderButton} />;
-});
-
-NewWorkflowButton.displayName = 'NewWorkflowButton';

invokeai/frontend/web/src/features/workflowLibrary/components/NewWorkflowConfirmationAlertDialog.tsx

@@ -1,63 +0,0 @@
-import { ConfirmationAlertDialog, Flex, Text, useDisclosure } from '@invoke-ai/ui-library';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
-import { workflowModeChanged } from 'features/nodes/store/workflowSlice';
-import { addToast } from 'features/system/store/systemSlice';
-import { makeToast } from 'features/system/util/makeToast';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-type Props = {
-  renderButton: (onClick: () => void) => JSX.Element;
-};
-
-export const NewWorkflowConfirmationAlertDialog = memo((props: Props) => {
-  const { t } = useTranslation();
-  const dispatch = useAppDispatch();
-  const { isOpen, onOpen, onClose } = useDisclosure();
-  const isTouched = useAppSelector((s) => s.workflow.isTouched);
-
-  const handleNewWorkflow = useCallback(() => {
-    dispatch(nodeEditorReset());
-    dispatch(workflowModeChanged('edit'));
-
-    dispatch(
-      addToast(
-        makeToast({
-          title: t('workflows.newWorkflowCreated'),
-          status: 'success',
-        })
-      )
-    );
-
-    onClose();
-  }, [dispatch, onClose, t]);
-
-  const onClick = useCallback(() => {
-    if (!isTouched) {
-      handleNewWorkflow();
-      return;
-    }
-    onOpen();
-  }, [handleNewWorkflow, isTouched, onOpen]);
-
-  return (
-    <>
-      {props.renderButton(onClick)}
-
-      <ConfirmationAlertDialog
-        isOpen={isOpen}
-        onClose={onClose}
-        title={t('nodes.newWorkflow')}
-        acceptCallback={handleNewWorkflow}
-      >
-        <Flex flexDir="column" gap={2}>
-          <Text>{t('nodes.newWorkflowDesc')}</Text>
-          <Text variant="subtext">{t('nodes.newWorkflowDesc2')}</Text>
-        </Flex>
-      </ConfirmationAlertDialog>
-    </>
-  );
-});
-
-NewWorkflowConfirmationAlertDialog.displayName = 'NewWorkflowConfirmationAlertDialog';

invokeai/frontend/web/src/features/workflowLibrary/components/UploadWorkflowButton.tsx

@@ -1,47 +0,0 @@
-import { Button } from '@invoke-ai/ui-library';
-import { useWorkflowLibraryModalContext } from 'features/workflowLibrary/context/useWorkflowLibraryModalContext';
-import { useLoadWorkflowFromFile } from 'features/workflowLibrary/hooks/useLoadWorkflowFromFile';
-import { memo, useCallback, useRef } from 'react';
-import { useDropzone } from 'react-dropzone';
-import { useTranslation } from 'react-i18next';
-import { PiUploadSimpleBold } from 'react-icons/pi';
-
-const UploadWorkflowButton = () => {
-  const { t } = useTranslation();
-  const resetRef = useRef<() => void>(null);
-  const { onClose } = useWorkflowLibraryModalContext();
-  const loadWorkflowFromFile = useLoadWorkflowFromFile({ resetRef, onSuccess: onClose });
-
-  const onDropAccepted = useCallback(
-    (files: File[]) => {
-      if (!files[0]) {
-        return;
-      }
-      loadWorkflowFromFile(files[0]);
-    },
-    [loadWorkflowFromFile]
-  );
-
-  const { getInputProps, getRootProps } = useDropzone({
-    accept: { 'application/json': ['.json'] },
-    onDropAccepted,
-    noDrag: true,
-    multiple: false,
-  });
-  return (
-    <>
-      <Button
-        aria-label={t('workflows.uploadWorkflow')}
-        tooltip={t('workflows.uploadWorkflow')}
-        leftIcon={<PiUploadSimpleBold />}
-        {...getRootProps()}
-        pointerEvents="auto"
-      >
-        {t('workflows.uploadWorkflow')}
-      </Button>
-      <input {...getInputProps()} />
-    </>
-  );
-};
-
-export default memo(UploadWorkflowButton);

invokeai/frontend/web/src/features/workflowLibrary/components/WorkflowLibraryButton.tsx

@@ -2,7 +2,7 @@ import { IconButton, useDisclosure } from '@invoke-ai/ui-library';
 import { WorkflowLibraryModalContext } from 'features/workflowLibrary/context/WorkflowLibraryModalContext';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiFolderOpenBold } from 'react-icons/pi';
+import { PiBooksBold } from 'react-icons/pi';
 
 import WorkflowLibraryModal from './WorkflowLibraryModal';
 
@@ -15,7 +15,7 @@ const WorkflowLibraryButton = () => {
       <IconButton
         aria-label={t('workflows.workflowLibrary')}
         tooltip={t('workflows.workflowLibrary')}
-        icon={<PiFolderOpenBold />}
+        icon={<PiBooksBold />}
         onClick={disclosure.onOpen}
         pointerEvents="auto"
       />

invokeai/frontend/web/src/features/workflowLibrary/components/WorkflowLibraryList.tsx

@@ -1,6 +1,5 @@
 import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
 import {
-  Box,
   Button,
   ButtonGroup,
   Combobox,
@@ -30,8 +29,6 @@ import type { SQLiteDirection, WorkflowRecordOrderBy } from 'services/api/types'
 import { useDebounce } from 'use-debounce';
 import { z } from 'zod';
 
-import UploadWorkflowButton from './UploadWorkflowButton';
-
 const PER_PAGE = 10;
 
 const zOrderBy = z.enum(['opened_at', 'created_at', 'updated_at', 'name']);
@@ -224,16 +221,11 @@ const WorkflowLibraryList = () => {
         <IAINoContentFallback label={t('workflows.noWorkflows')} />
       )}
       <Divider />
-
-      <Flex w="full">
-        <Box flex="1">
-          <UploadWorkflowButton />
-        </Box>
-        <Box flex="1" textAlign="center">
-          {data && <WorkflowLibraryPagination data={data} page={page} setPage={setPage} />}
-        </Box>
-        <Box flex="1"></Box>
-      </Flex>
+      {data && (
+        <Flex w="full" justifyContent="space-around">
+          <WorkflowLibraryPagination data={data} page={page} setPage={setPage} />
+        </Flex>
+      )}
     </>
   );
 };

invokeai/frontend/web/src/features/workflowLibrary/components/WorkflowLibraryMenu/NewWorkflowMenuItem.tsx

@@ -1,22 +1,60 @@
-import { MenuItem } from '@invoke-ai/ui-library';
-import { NewWorkflowConfirmationAlertDialog } from 'features/workflowLibrary/components/NewWorkflowConfirmationAlertDialog';
+import { ConfirmationAlertDialog, Flex, MenuItem, Text, useDisclosure } from '@invoke-ai/ui-library';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
+import { addToast } from 'features/system/store/systemSlice';
+import { makeToast } from 'features/system/util/makeToast';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiFilePlusBold } from 'react-icons/pi';
+import { PiFlowArrowBold } from 'react-icons/pi';
 
-export const NewWorkflowMenuItem = memo(() => {
+const NewWorkflowMenuItem = () => {
   const { t } = useTranslation();
+  const dispatch = useAppDispatch();
+  const { isOpen, onOpen, onClose } = useDisclosure();
+  const isTouched = useAppSelector((s) => s.workflow.isTouched);
 
-  const renderButton = useCallback(
-    (onClick: () => void) => (
-      <MenuItem as="button" icon={<PiFilePlusBold />} onClick={onClick}>
+  const handleNewWorkflow = useCallback(() => {
+    dispatch(nodeEditorReset());
+
+    dispatch(
+      addToast(
+        makeToast({
+          title: t('workflows.newWorkflowCreated'),
+          status: 'success',
+        })
+      )
+    );
+
+    onClose();
+  }, [dispatch, onClose, t]);
+
+  const onClick = useCallback(() => {
+    if (!isTouched) {
+      handleNewWorkflow();
+      return;
+    }
+    onOpen();
+  }, [handleNewWorkflow, isTouched, onOpen]);
+
+  return (
+    <>
+      <MenuItem as="button" icon={<PiFlowArrowBold />} onClick={onClick}>
         {t('nodes.newWorkflow')}
       </MenuItem>
-    ),
-    [t]
+
+      <ConfirmationAlertDialog
+        isOpen={isOpen}
+        onClose={onClose}
+        title={t('nodes.newWorkflow')}
+        acceptCallback={handleNewWorkflow}
+      >
+        <Flex flexDir="column" gap={2}>
+          <Text>{t('nodes.newWorkflowDesc')}</Text>
+          <Text variant="subtext">{t('nodes.newWorkflowDesc2')}</Text>
+        </Flex>
+      </ConfirmationAlertDialog>
+    </>
   );
+};
 
-  return <NewWorkflowConfirmationAlertDialog renderButton={renderButton} />;
-});
-
-NewWorkflowMenuItem.displayName = 'NewWorkflowMenuItem';
+export default memo(NewWorkflowMenuItem);

invokeai/frontend/web/src/features/workflowLibrary/components/WorkflowLibraryMenu/WorkflowLibraryMenu.tsx

@@ -8,7 +8,7 @@ import {
   useGlobalMenuClose,
 } from '@invoke-ai/ui-library';
 import DownloadWorkflowMenuItem from 'features/workflowLibrary/components/WorkflowLibraryMenu/DownloadWorkflowMenuItem';
-import { NewWorkflowMenuItem } from 'features/workflowLibrary/components/WorkflowLibraryMenu/NewWorkflowMenuItem';
+import NewWorkflowMenuItem from 'features/workflowLibrary/components/WorkflowLibraryMenu/NewWorkflowMenuItem';
 import SaveWorkflowAsMenuItem from 'features/workflowLibrary/components/WorkflowLibraryMenu/SaveWorkflowAsMenuItem';
 import SaveWorkflowMenuItem from 'features/workflowLibrary/components/WorkflowLibraryMenu/SaveWorkflowMenuItem';
 import SettingsMenuItem from 'features/workflowLibrary/components/WorkflowLibraryMenu/SettingsMenuItem';