{release} 3.6.4

## What type of PR is this? (check all applicable)

- [ ] Refactor
- [ ] Feature
- [ X ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission


## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ X ] No, because: It's small

      
## Have you updated all relevant documentation?
- [ ] Yes
- [ X ] No


## Description
This pulls out some of the updates from the WIP Seamless branch that has
yet to be completed, and hardcodes values that are exposed in that
branch. Given that seamless currently does not generate seamless
textures, and this fix results in seamless outputs, it's an improvement
even if it doesn't resolve this in a "perfect" way that exposes all
variables to the end user.

better over perfect.


![f07b7e49-80c2-4659-bb36-d50ec80b1f8b](https://github.com/invoke-ai/InvokeAI/assets/31807370/36a40bd9-8fc4-41d5-bd1e-209fc828987e)

docs/features/CONTROLNET.md

@@ -94,6 +94,8 @@ 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/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.                                                                                                                         |
-| Openpose Processor                                            | Applies Openpose processing to image                                                                                                                 |
+| DW 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.                                                                                                                     |

invokeai/app/invocations/controlnet_image_processors.py

@@ -17,7 +17,6 @@ from controlnet_aux import (
     MidasDetector,
     MLSDdetector,
     NormalBaeDetector,
-    OpenposeDetector,
     PidiNetDetector,
     SamDetector,
     ZoeDetector,
@@ -31,6 +30,7 @@ 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 (
@@ -276,31 +276,6 @@ 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",
@@ -624,7 +599,7 @@ 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):
+    def run_processor(self, image: Image.Image):
         depth_anything_detector = DepthAnythingDetector()
         depth_anything_detector.load_model(model_size=self.model_size)
 
@@ -633,3 +608,30 @@ 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/backend/image_util/dw_openpose/__init__.py

@@ -0,0 +1,81 @@
+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

@@ -0,0 +1,128 @@
+# 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

@@ -0,0 +1,361 @@
+# 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

@@ -0,0 +1,155 @@
+# 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

@@ -0,0 +1,67 @@
+# 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/model_management/seamless.py

@@ -1,10 +1,11 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import List, Union
+from typing import Callable, List, Union
 
 import torch.nn as nn
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 
 
 def _conv_forward_asymmetric(self, input, weight, bias):
@@ -26,70 +27,50 @@ 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:
-        to_restore = []
-
         for m_name, m in model.named_modules():
-            if isinstance(model, UNet2DConditionModel):
-                if ".attentions." in m_name:
+            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)
+
+                # Could be configurable to allow skipping arbitrary numbers of down blocks
+                if block_num >= len(model.down_blocks):
                     continue
 
-                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:
+                # Skip the second resnet (could be configurable)
+                if resnet_num > 0:
                     continue
 
-                if False and ".downsamplers." in m_name:
+                # Skip Conv2d layers (could be configurable)
+                if submodule_name == "conv2":
                     continue
 
-                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
+            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 ".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)
+            to_restore.append((m, m._conv_forward))
+            m._conv_forward = _conv_forward_asymmetric.__get__(m, nn.Conv2d)
 
         yield
 

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

@@ -69,7 +69,7 @@
         "random": "Zufall",
         "batch": "Stapel-Manager",
         "advanced": "Erweitert",
-        "unifiedCanvas": "Einheitliche Leinwand",
+        "unifiedCanvas": "Leinwand",
         "openInNewTab": "In einem neuem Tab öffnen",
         "statusProcessing": "wird bearbeitet",
         "linear": "Linear",
@@ -127,7 +127,7 @@
         "galleryImageResetSize": "Größe zurücksetzen",
         "gallerySettings": "Galerie-Einstellungen",
         "maintainAspectRatio": "Seitenverhältnis beibehalten",
-        "autoSwitchNewImages": "Automatisch zu neuen Bildern wechseln",
+        "autoSwitchNewImages": "Auto-Wechsel zu neuen Bildern",
         "singleColumnLayout": "Einspaltiges Layout",
         "allImagesLoaded": "Alle Bilder geladen",
         "loadMore": "Mehr laden",
@@ -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 für die Leinwand aus"
+            "desc": "Wählt den Radiergummi aus"
         },
         "decreaseBrushSize": {
             "title": "Pinselgröße verkleinern",
@@ -330,7 +330,7 @@
         },
         "downloadImage": {
             "title": "Bild herunterladen",
-            "desc": "Aktuelle Leinwand herunterladen"
+            "desc": "Aktuelles Bild herunterladen"
         },
         "undoStroke": {
             "title": "Pinselstrich rückgängig machen",
@@ -564,8 +564,8 @@
         "img2imgStrength": "Bild-zu-Bild-Stärke",
         "toggleLoopback": "Loopback umschalten",
         "sendTo": "Senden an",
-        "sendToImg2Img": "Senden an Bild zu Bild",
-        "sendToUnifiedCanvas": "Senden an Unified Canvas",
+        "sendToImg2Img": "Senden an Bild-zu-Bild",
+        "sendToUnifiedCanvas": "Senden an Leinwand",
         "copyImageToLink": "Bild-Link kopieren",
         "downloadImage": "Bild herunterladen",
         "openInViewer": "Im Viewer öffnen",
@@ -604,7 +604,9 @@
         "resetComplete": "Die Web-Oberfläche wurde zurückgesetzt.",
         "models": "Modelle",
         "useSlidersForAll": "Schieberegler für alle Optionen verwenden",
-        "showAdvancedOptions": "Erweiterte Optionen anzeigen"
+        "showAdvancedOptions": "Erweiterte Optionen anzeigen",
+        "alternateCanvasLayout": "Alternatives Leinwand-Layout",
+        "clearIntermediatesDesc1": "Das Löschen der Zwischenprodukte setzt Leinwand und ControlNet zurück."
     },
     "toast": {
         "tempFoldersEmptied": "Temp-Ordner geleert",
@@ -618,7 +620,7 @@
         "imageSavedToGallery": "Bild in die Galerie gespeichert",
         "canvasMerged": "Leinwand zusammengeführt",
         "sentToImageToImage": "Gesendet an Bild zu Bild",
-        "sentToUnifiedCanvas": "Gesendet an Unified Canvas",
+        "sentToUnifiedCanvas": "Gesendet an Leinwand",
         "parametersSet": "Parameter festlegen",
         "parametersNotSet": "Parameter nicht festgelegt",
         "parametersNotSetDesc": "Keine Metadaten für dieses Bild gefunden.",
@@ -635,7 +637,21 @@
         "metadataLoadFailed": "Metadaten konnten nicht geladen werden",
         "initialImageSet": "Ausgangsbild festgelegt",
         "initialImageNotSet": "Ausgangsbild nicht festgelegt",
-        "initialImageNotSetDesc": "Ausgangsbild konnte nicht geladen werden"
+        "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"
     },
     "tooltip": {
         "feature": {
@@ -648,7 +664,7 @@
             "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": "Steuert die Behandlung von sichtbaren Übergängen, die zwischen den erzeugten Bildern auf der Leinwand auftreten.",
+            "seamCorrection": "Behandlung von sichtbaren Übergängen, die zwischen den erzeugten Bildern auftreten.",
             "infillAndScaling": "Verwalten Sie Infill-Methoden (für maskierte oder gelöschte Bereiche der Leinwand) und Skalierung (nützlich für kleine Begrenzungsrahmengrößen)."
         }
     },
@@ -659,17 +675,17 @@
         "maskingOptions": "Maskierungsoptionen",
         "enableMask": "Maske aktivieren",
         "preserveMaskedArea": "Maskierten Bereich bewahren",
-        "clearMask": "Maske löschen",
+        "clearMask": "Maske löschen (Shift+C)",
         "brush": "Pinsel",
         "eraser": "Radierer",
         "fillBoundingBox": "Begrenzungsrahmen füllen",
         "eraseBoundingBox": "Begrenzungsrahmen löschen",
-        "colorPicker": "Farbpipette",
+        "colorPicker": "Pipette",
         "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",
@@ -683,15 +699,15 @@
         "darkenOutsideSelection": "Außerhalb der Auswahl verdunkeln",
         "autoSaveToGallery": "Automatisch in Galerie speichern",
         "saveBoxRegionOnly": "Nur Auswahlbox speichern",
-        "limitStrokesToBox": "Striche auf Box beschränken",
-        "showCanvasDebugInfo": "Zusätzliche Informationen zur Leinwand anzeigen",
+        "limitStrokesToBox": "Striche auf Auswahl beschränken",
+        "showCanvasDebugInfo": "Zusätzliche Informationen anzeigen",
         "clearCanvasHistory": "Leinwand-Verlauf löschen",
         "clearHistory": "Verlauf löschen",
-        "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?",
+        "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?",
         "emptyTempImageFolder": "Temp-Image Ordner leeren",
         "emptyFolder": "Leerer Ordner",
-        "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.",
+        "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.",
         "emptyTempImagesFolderConfirm": "Sind Sie sicher, dass Sie den temporären Ordner leeren wollen?",
         "activeLayer": "Aktive Ebene",
         "canvasScale": "Leinwand Maßstab",
@@ -708,7 +724,7 @@
         "discardAll": "Alles verwerfen",
         "betaClear": "Löschen",
         "betaDarkenOutside": "Außen abdunkeln",
-        "betaLimitToBox": "Begrenzung auf das Feld",
+        "betaLimitToBox": "Auf Auswahl begrenzen",
         "betaPreserveMasked": "Maskiertes bewahren",
         "antialiasing": "Kantenglättung",
         "showResultsOn": "Zeige Ergebnisse (An)",
@@ -746,7 +762,7 @@
         "autoAddBoard": "Automatisches Hinzufügen zum Ordner",
         "topMessage": "Dieser Ordner enthält Bilder die in den folgenden Funktionen verwendet werden:",
         "move": "Bewegen",
-        "menuItemAutoAdd": "Automatisches Hinzufügen zu diesem Ordner",
+        "menuItemAutoAdd": "Auto-Hinzufügen zu diesem Ordner",
         "myBoard": "Meine Ordner",
         "searchBoard": "Ordner durchsuchen...",
         "noMatching": "Keine passenden Ordner",
@@ -826,7 +842,6 @@
         "pidi": "PIDI",
         "normalBae": "Normales BAE",
         "mlsdDescription": "Minimalistischer Liniensegmentdetektor",
-        "openPoseDescription": "Schätzung der menschlichen Pose mit Openpose",
         "control": "Kontrolle",
         "coarse": "Grob",
         "crop": "Zuschneiden",
@@ -839,10 +854,9 @@
         "lineartAnimeDescription": "Lineart-Verarbeitung im Anime-Stil",
         "minConfidence": "Minimales Vertrauen",
         "megaControl": "Mega-Kontrolle",
-        "autoConfigure": "Prozessor automatisch konfigurieren",
+        "autoConfigure": "Prozessor Auto-konfig",
         "normalBaeDescription": "Normale BAE-Verarbeitung",
         "noneDescription": "Es wurde keine Verarbeitung angewendet",
-        "openPose": "Openpose / \"Pose nutzen\"",
         "lineartAnime": "Lineart Anime / \"Strichzeichnung Anime\"",
         "mediapipeFaceDescription": "Gesichtserkennung mit Mediapipe",
         "canny": "\"Canny\"",
@@ -944,7 +958,7 @@
         "initImage": "Erstes Bild",
         "variations": "Seed-Gewichtungs-Paare",
         "vae": "VAE",
-        "workflow": "Arbeitsablauf",
+        "workflow": "Workflow",
         "scheduler": "Planer",
         "noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden",
         "recallParameters": "Parameter wiederherstellen"
@@ -1056,6 +1070,20 @@
                 "\"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"
         }
     },
     "ui": {
@@ -1160,10 +1188,10 @@
         "outputFieldInInput": "Ausgabefeld im Eingang",
         "problemReadingWorkflow": "Problem beim Lesen des Arbeitsablaufs vom Bild",
         "reloadNodeTemplates": "Knoten-Vorlagen neu laden",
-        "newWorkflow": "Neuer Arbeitsablauf",
+        "newWorkflow": "Neuer Arbeitsablauf / Workflow",
         "newWorkflowDesc": "Einen neuen Arbeitsablauf erstellen?",
         "noFieldsLinearview": "Keine Felder zur linearen Ansicht hinzugefügt",
-        "clearWorkflow": "Arbeitsablauf löschen",
+        "clearWorkflow": "Workflow löschen",
         "clearWorkflowDesc": "Diesen Arbeitsablauf löschen und neu starten?",
         "noConnectionInProgress": "Es besteht keine Verbindung",
         "notes": "Anmerkungen",
@@ -1220,8 +1248,8 @@
         "stringDescription": "Zeichenfolgen (Strings) sind Text.",
         "fieldTypesMustMatch": "Feldtypen müssen übereinstimmen",
         "fitViewportNodes": "An Ansichtsgröße anpassen",
-        "missingCanvaInitMaskImages": "Fehlende Startbilder und Masken auf der Arbeitsfläche",
-        "missingCanvaInitImage": "Fehlendes Startbild auf der Arbeitsfläche",
+        "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...",
@@ -1321,7 +1349,7 @@
         "workflows": "Arbeitsabläufe",
         "noSystemWorkflows": "Keine System-Arbeitsabläufe",
         "workflowName": "Arbeitsablauf-Name",
-        "workflowIsOpen": "Arbeitsablauf ist offen",
+        "workflowIsOpen": "Arbeitsablauf ist geöffnet",
         "saveWorkflowAs": "Arbeitsablauf speichern als",
         "searchWorkflows": "Suche Arbeitsabläufe",
         "newWorkflowCreated": "Neuer Arbeitsablauf erstellt",

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

@@ -235,6 +235,9 @@
         "fill": "Fill",
         "h": "H",
         "handAndFace": "Hand and Face",
+        "face": "Face",
+        "body": "Body",
+        "hands": "Hands",
         "hed": "HED",
         "hedDescription": "Holistically-Nested Edge Detection",
         "hideAdvanced": "Hide Advanced",
@@ -261,8 +264,8 @@
         "noneDescription": "No processing applied",
         "normalBae": "Normal BAE",
         "normalBaeDescription": "Normal BAE processing",
-        "openPose": "Openpose",
-        "openPoseDescription": "Human pose estimation using Openpose",
+        "dwOpenpose": "DW Openpose",
+        "dwOpenposeDescription": "Human pose estimation using DW Openpose",
         "pidi": "PIDI",
         "pidiDescription": "PIDI image processing",
         "processor": "Processor",

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

@@ -795,7 +795,8 @@
         "workflowDeleted": "Flusso di lavoro eliminato",
         "problemRetrievingWorkflow": "Problema nel recupero del flusso di lavoro",
         "resetInitialImage": "Reimposta l'immagine iniziale",
-        "uploadInitialImage": "Carica l'immagine iniziale"
+        "uploadInitialImage": "Carica l'immagine iniziale",
+        "problemDownloadingImage": "Impossibile scaricare l'immagine"
     },
     "tooltip": {
         "feature": {
@@ -1134,7 +1135,10 @@
         "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}})"
+        "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."
     },
     "boards": {
         "autoAddBoard": "Aggiungi automaticamente bacheca",
@@ -1191,7 +1195,6 @@
         "f": "F",
         "h": "A",
         "prompt": "Prompt",
-        "openPoseDescription": "Stima della posa umana utilizzando Openpose",
         "resizeMode": "Ridimensionamento",
         "weight": "Peso",
         "selectModel": "Seleziona un modello",
@@ -1672,7 +1675,9 @@
         "downloadWorkflow": "Salva su file",
         "uploadWorkflow": "Carica da file",
         "projectWorkflows": "Flussi di lavoro del progetto",
-        "noWorkflows": "Nessun flusso di lavoro"
+        "noWorkflows": "Nessun flusso di lavoro",
+        "workflowCleared": "Flusso di lavoro cancellato",
+        "saveWorkflowToProject": "Salva flusso di lavoro nel progetto"
     },
     "app": {
         "storeNotInitialized": "Il negozio non è inizializzato"

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

@@ -555,7 +555,6 @@
         "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

@@ -333,7 +333,6 @@
         "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",
@@ -370,7 +369,6 @@
         "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

@@ -1033,7 +1033,6 @@
         "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",
@@ -1072,7 +1071,6 @@
         "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

@@ -1155,7 +1155,6 @@
         "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

@@ -259,7 +259,6 @@
         "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",
@@ -273,7 +272,6 @@
         "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

@@ -1143,7 +1143,6 @@
         "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",
@@ -1207,7 +1206,6 @@
         "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 { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { appInfoApi } from 'services/api/endpoints/appInfo';
 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 (receivedOpenAPISchema.fulfilled.match(action)) {
+  if (appInfoApi.endpoints.getOpenAPISchema.matchFulfilled(action)) {
     return {
       ...action,
       payload: '<OpenAPI schema omitted>',

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

@@ -23,6 +23,7 @@ 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,
@@ -47,7 +48,6 @@ 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
-addReceivedOpenAPISchemaListener();
+addGetOpenAPISchemaListener();
 
 // Workflows
 addWorkflowLoadRequestedListener();

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema.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 { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { appInfoApi } from 'services/api/endpoints/appInfo';
 
 import { startAppListening } from '..';
 
-export const addReceivedOpenAPISchemaListener = () => {
+export const addGetOpenAPISchemaListener = () => {
   startAppListening({
-    actionCreator: receivedOpenAPISchema.fulfilled,
+    matcher: appInfoApi.endpoints.getOpenAPISchema.matchFulfilled,
     effect: (action, { dispatch, getState }) => {
       const log = logger('system');
       const schemaJSON = action.payload;
 
-      log.debug({ schemaJSON }, 'Received OpenAPI schema');
+      log.debug({ schemaJSON: parseify(schemaJSON) }, 'Received OpenAPI schema');
       const { nodesAllowlist, nodesDenylist } = getState().config;
 
       const nodeTemplates = parseSchema(schemaJSON, nodesAllowlist, nodesDenylist);
@@ -26,10 +26,14 @@ export const addReceivedOpenAPISchemaListener = () => {
   });
 
   startAppListening({
-    actionCreator: receivedOpenAPISchema.rejected,
+    matcher: appInfoApi.endpoints.getOpenAPISchema.matchRejected,
     effect: (action) => {
-      const log = logger('system');
-      log.error({ error: parseify(action.error) }, 'Problem retrieving OpenAPI Schema');
+      // 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');
+      }
     },
   });
 };

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

@@ -1,10 +1,9 @@
 import { logger } from 'app/logging/logger';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
-import { isEqual, size } from 'lodash-es';
+import { isEqual } 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 '../..';
@@ -77,17 +76,4 @@ 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/features/controlAdapters/components/ControlAdapterProcessorComponent.tsx

@@ -6,6 +6,7 @@ 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';
@@ -13,7 +14,6 @@ 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 === 'openpose_image_processor') {
-    return <OpenposeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
+  if (processorNode.type === 'dw_openpose_image_processor') {
+    return <DWOpenposeProcessor controlNetId={id} processorNode={processorNode} isEnabled={isEnabled} />;
   }
 
   if (processorNode.type === 'pidi_image_processor') {

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

@@ -0,0 +1,92 @@
+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

@@ -1,92 +0,0 @@
-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

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

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

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 };
+      state.loras[id] = { id, model_name, base_model, weight, isEnabled: true };
     },
     loraRemoved: (state, action: PayloadAction<string>) => {
       const id = action.payload;

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

@@ -1,7 +1,6 @@
 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';
@@ -11,6 +10,7 @@ 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 isReady = useAppSelector((s) => s.nodes.isReady);
+  const { data, isLoading } = useGetOpenAPISchemaQuery();
   const { t } = useTranslation();
   return (
     <Flex
@@ -53,7 +53,7 @@ const NodeEditor = () => {
       justifyContent="center"
     >
       <AnimatePresence>
-        {isReady && (
+        {data && (
           <motion.div initial={initial} animate={animate} exit={exit} style={isReadyMotionStyles}>
             <Flow />
             <AddNodePopover />
@@ -65,7 +65,7 @@ const NodeEditor = () => {
         )}
       </AnimatePresence>
       <AnimatePresence>
-        {!isReady && (
+        {isLoading && (
           <motion.div initial={initial} animate={animate} exit={exit} style={notIsReadyMotionStyles}>
             <Flex
               layerStyle="first"

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

@@ -1,17 +1,16 @@
 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 { receivedOpenAPISchema } from 'services/api/thunks/schema';
+import { useLazyGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 
 const ReloadNodeTemplatesButton = () => {
   const { t } = useTranslation();
-  const dispatch = useAppDispatch();
+  const [_getOpenAPISchema] = useLazyGetOpenAPISchemaQuery();
 
   const handleReloadSchema = useCallback(() => {
-    dispatch(receivedOpenAPISchema());
-  }, [dispatch]);
+    _getOpenAPISchema();
+  }, [_getOpenAPISchema]);
 
   return (
     <Button

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

@@ -7,18 +7,22 @@ import LinearViewField from 'features/nodes/components/flow/nodes/Invocation/fie
 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();
 
   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">
-          {fields.length ? (
+          {isLoading ? (
+            <IAINoContentFallback label={t('nodes.loadingNodes')} icon={null} />
+          ) : fields.length ? (
             fields.map(({ nodeId, fieldName }) => (
               <LinearViewField key={`${nodeId}.${fieldName}`} nodeId={nodeId} fieldName={fieldName} />
             ))

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

@@ -2,7 +2,6 @@ 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,
@@ -65,7 +64,6 @@ import {
   SelectionMode,
   updateEdge,
 } from 'reactflow';
-import { receivedOpenAPISchema } from 'services/api/thunks/schema';
 import {
   socketGeneratorProgress,
   socketInvocationComplete,
@@ -92,7 +90,6 @@ export const initialNodesState: NodesState = {
   _version: 1,
   nodes: [],
   edges: [],
-  isReady: false,
   connectionStartParams: null,
   connectionStartFieldType: null,
   connectionMade: false,
@@ -677,10 +674,6 @@ 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(
@@ -752,9 +745,6 @@ export const nodesSlice = createSlice({
         });
       }
     });
-    builder.addCase(nodeTemplatesBuilt, (state) => {
-      state.isReady = true;
-    });
   },
 });
 
@@ -871,7 +861,6 @@ export const nodesPersistConfig: PersistConfig<NodesState> = {
     'connectionStartFieldType',
     'selectedNodes',
     'selectedEdges',
-    'isReady',
     'nodesToCopy',
     'edgesToCopy',
     'connectionMade',

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

@@ -26,7 +26,6 @@ export type NodesState = {
   selectedEdges: string[];
   nodeExecutionStates: Record<string, NodeExecutionState>;
   viewport: Viewport;
-  isReady: boolean;
   nodesToCopy: AnyNode[];
   edgesToCopy: InvocationNodeEdge[];
   isAddNodePopoverOpen: boolean;

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

@@ -0,0 +1,47 @@
+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/WorkflowLibraryList.tsx

@@ -1,5 +1,6 @@
 import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
 import {
+  Box,
   Button,
   ButtonGroup,
   Combobox,
@@ -29,6 +30,8 @@ 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']);
@@ -221,11 +224,16 @@ const WorkflowLibraryList = () => {
         <IAINoContentFallback label={t('workflows.noWorkflows')} />
       )}
       <Divider />
-      {data && (
-        <Flex w="full" justifyContent="space-around">
-          <WorkflowLibraryPagination data={data} page={page} setPage={setPage} />
-        </Flex>
-      )}
+
+      <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>
     </>
   );
 };

invokeai/frontend/web/src/features/workflowLibrary/hooks/useLoadWorkflowFromFile.tsx

@@ -10,11 +10,12 @@ import { useTranslation } from 'react-i18next';
 
 type useLoadWorkflowFromFileOptions = {
   resetRef: RefObject<() => void>;
+  onSuccess?: () => void;
 };
 
 type UseLoadWorkflowFromFile = (options: useLoadWorkflowFromFileOptions) => (file: File | null) => void;
 
-export const useLoadWorkflowFromFile: UseLoadWorkflowFromFile = ({ resetRef }) => {
+export const useLoadWorkflowFromFile: UseLoadWorkflowFromFile = ({ resetRef, onSuccess }) => {
   const dispatch = useAppDispatch();
   const logger = useLogger('nodes');
   const { t } = useTranslation();
@@ -31,6 +32,7 @@ export const useLoadWorkflowFromFile: UseLoadWorkflowFromFile = ({ resetRef }) =
           const parsedJSON = JSON.parse(String(rawJSON));
           dispatch(workflowLoadRequested({ workflow: parsedJSON, asCopy: true }));
           dispatch(workflowLoadedFromFile());
+          onSuccess && onSuccess();
         } catch (e) {
           // There was a problem reading the file
           logger.error(t('nodes.unableToLoadWorkflow'));
@@ -51,7 +53,7 @@ export const useLoadWorkflowFromFile: UseLoadWorkflowFromFile = ({ resetRef }) =
       // Reset the file picker internal state so that the same file can be loaded again
       resetRef.current?.();
     },
-    [dispatch, logger, resetRef, t]
+    [dispatch, logger, resetRef, t, onSuccess]
   );
 
   return loadWorkflowFromFile;

invokeai/frontend/web/src/services/api/endpoints/appInfo.ts

@@ -1,3 +1,5 @@
+import { $openAPISchemaUrl } from 'app/store/nanostores/openAPISchemaUrl';
+import type { OpenAPIV3_1 } from 'openapi-types';
 import type { paths } from 'services/api/schema';
 import type { AppConfig, AppDependencyVersions, AppVersion } from 'services/api/types';
 
@@ -57,6 +59,14 @@ export const appInfoApi = api.injectEndpoints({
       }),
       invalidatesTags: ['InvocationCacheStatus'],
     }),
+    getOpenAPISchema: build.query<OpenAPIV3_1.Document, void>({
+      query: () => {
+        const openAPISchemaUrl = $openAPISchemaUrl.get();
+        const url = openAPISchemaUrl ? openAPISchemaUrl : `${window.location.href.replace(/\/$/, '')}/openapi.json`;
+        return url;
+      },
+      providesTags: ['Schema'],
+    }),
   }),
 });
 
@@ -68,4 +78,6 @@ export const {
   useDisableInvocationCacheMutation,
   useEnableInvocationCacheMutation,
   useGetInvocationCacheStatusQuery,
+  useGetOpenAPISchemaQuery,
+  useLazyGetOpenAPISchemaQuery,
 } = appInfoApi;

invokeai/frontend/web/src/services/api/index.ts

@@ -1,3 +1,4 @@
+import type { FetchBaseQueryArgs } from '@reduxjs/toolkit/dist/query/fetchBaseQuery';
 import type { BaseQueryFn, FetchArgs, FetchBaseQueryError, TagDescription } from '@reduxjs/toolkit/query/react';
 import { createApi, fetchBaseQuery } from '@reduxjs/toolkit/query/react';
 import { $authToken } from 'app/store/nanostores/authToken';
@@ -35,6 +36,7 @@ export const tagTypes = [
   'SDXLRefinerModel',
   'Workflow',
   'WorkflowsRecent',
+  'Schema',
   // This is invalidated on reconnect. It should be used for queries that have changing data,
   // especially related to the queue and generation.
   'FetchOnReconnect',
@@ -51,7 +53,7 @@ const dynamicBaseQuery: BaseQueryFn<string | FetchArgs, unknown, FetchBaseQueryE
   const authToken = $authToken.get();
   const projectId = $projectId.get();
 
-  const rawBaseQuery = fetchBaseQuery({
+  const fetchBaseQueryArgs: FetchBaseQueryArgs = {
     baseUrl: baseUrl ? `${baseUrl}/api/v1` : `${window.location.href.replace(/\/$/, '')}/api/v1`,
     prepareHeaders: (headers) => {
       if (authToken) {
@@ -63,7 +65,17 @@ const dynamicBaseQuery: BaseQueryFn<string | FetchArgs, unknown, FetchBaseQueryE
 
       return headers;
     },
-  });
+  };
+
+  // When fetching the openapi.json, we need to remove circular references from the JSON.
+  if (
+    (args instanceof Object && args.url.includes('openapi.json')) ||
+    (typeof args === 'string' && args.includes('openapi.json'))
+  ) {
+    fetchBaseQueryArgs.jsonReplacer = getCircularReplacer();
+  }
+
+  const rawBaseQuery = fetchBaseQuery(fetchBaseQueryArgs);
 
   return rawBaseQuery(args, api, extraOptions);
 };
@@ -74,3 +86,25 @@ export const api = createApi({
   tagTypes,
   endpoints: () => ({}),
 });
+
+function getCircularReplacer() {
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  const ancestors: Record<string, any>[] = [];
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  return function (key: string, value: any) {
+    if (typeof value !== 'object' || value === null) {
+      return value;
+    }
+    // `this` is the object that value is contained in, i.e., its direct parent.
+    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
+    // @ts-ignore don't think it's possible to not have TS complain about this...
+    while (ancestors.length > 0 && ancestors.at(-1) !== this) {
+      ancestors.pop();
+    }
+    if (ancestors.includes(value)) {
+      return '[Circular]';
+    }
+    ancestors.push(value);
+    return value;
+  };
+}

invokeai/frontend/web/src/services/api/thunks/schema.ts

@@ -1,40 +0,0 @@
-import { createAsyncThunk } from '@reduxjs/toolkit';
-import { $openAPISchemaUrl } from 'app/store/nanostores/openAPISchemaUrl';
-
-function getCircularReplacer() {
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  const ancestors: Record<string, any>[] = [];
-  // eslint-disable-next-line @typescript-eslint/no-explicit-any
-  return function (key: string, value: any) {
-    if (typeof value !== 'object' || value === null) {
-      return value;
-    }
-    // `this` is the object that value is contained in, i.e., its direct parent.
-    // eslint-disable-next-line @typescript-eslint/ban-ts-comment
-    // @ts-ignore don't think it's possible to not have TS complain about this...
-    while (ancestors.length > 0 && ancestors.at(-1) !== this) {
-      ancestors.pop();
-    }
-    if (ancestors.includes(value)) {
-      return '[Circular]';
-    }
-    ancestors.push(value);
-    return value;
-  };
-}
-
-export const receivedOpenAPISchema = createAsyncThunk('nodes/receivedOpenAPISchema', async (_, { rejectWithValue }) => {
-  try {
-    const openAPISchemaUrl = $openAPISchemaUrl.get();
-
-    const url = openAPISchemaUrl ? openAPISchemaUrl : `${window.location.href.replace(/\/$/, '')}/openapi.json`;
-    const response = await fetch(url);
-    const openAPISchema = await response.json();
-
-    const schemaJSON = JSON.parse(JSON.stringify(openAPISchema, getCircularReplacer()));
-
-    return schemaJSON;
-  } catch (error) {
-    return rejectWithValue({ error });
-  }
-});

invokeai/frontend/web/src/services/api/types.ts

@@ -156,7 +156,7 @@ export type MediapipeFaceProcessorInvocation = s['MediapipeFaceProcessorInvocati
 export type MidasDepthImageProcessorInvocation = s['MidasDepthImageProcessorInvocation'];
 export type MlsdImageProcessorInvocation = s['MlsdImageProcessorInvocation'];
 export type NormalbaeImageProcessorInvocation = s['NormalbaeImageProcessorInvocation'];
-export type OpenposeImageProcessorInvocation = s['OpenposeImageProcessorInvocation'];
+export type DWOpenposeImageProcessorInvocation = s['DWOpenposeImageProcessorInvocation'];
 export type PidiImageProcessorInvocation = s['PidiImageProcessorInvocation'];
 export type ZoeDepthImageProcessorInvocation = s['ZoeDepthImageProcessorInvocation'];
 

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "3.6.3"
+__version__ = "3.6.4"

pyproject.toml

@@ -37,7 +37,7 @@ dependencies = [
   "clip_anytorch==2.5.2",       # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
   "compel==2.0.2",
   "controlnet-aux==0.0.7",
-  "diffusers[torch]==0.26.2",
+  "diffusers[torch]==0.26.3",
   "invisible-watermark==0.2.0", # needed to install SDXL base and refiner using their repo_ids
   "mediapipe==0.10.7",          # needed for "mediapipeface" controlnet model
   "numpy==1.26.4",              # >1.24.0 is needed to use the 'strict' argument to np.testing.assert_array_equal()