import torch import comfy.utils import comfy.model_management import numpy as np from tqdm import tqdm from typing_extensions import override from comfy_api.latest import ComfyExtension, io from comfy_extras.pose.keypoint_draw import KeypointDraw from comfy_extras.nodes_lotus import LotusConditioning def _preprocess_keypoints(kp_raw, sc_raw): """Insert neck keypoint and remap from MMPose to OpenPose ordering. Returns (kp, sc) where kp has shape (134, 2) and sc has shape (134,). Layout: 0-17 body (18 kp, OpenPose order) 18-23 feet (6 kp) 24-91 face (68 kp) 92-112 right hand (21 kp) 113-133 left hand (21 kp) """ kp = np.array(kp_raw, dtype=np.float32) sc = np.array(sc_raw, dtype=np.float32) if len(kp) >= 17: neck = (kp[5] + kp[6]) / 2 neck_score = min(sc[5], sc[6]) if sc[5] > 0.3 and sc[6] > 0.3 else 0 kp = np.insert(kp, 17, neck, axis=0) sc = np.insert(sc, 17, neck_score) mmpose_idx = np.array([17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3]) openpose_idx = np.array([ 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17]) tmp_kp, tmp_sc = kp.copy(), sc.copy() tmp_kp[openpose_idx] = kp[mmpose_idx] tmp_sc[openpose_idx] = sc[mmpose_idx] kp, sc = tmp_kp, tmp_sc return kp, sc def _to_openpose_frames(all_keypoints, all_scores, height, width): """Convert raw keypoint lists to a list of OpenPose-style frame dicts. Each frame dict contains: canvas_width, canvas_height, people: list of person dicts with keys: pose_keypoints_2d - 18 body kp as flat [x,y,score,...] (absolute pixels) foot_keypoints_2d - 6 foot kp as flat [x,y,score,...] (absolute pixels) face_keypoints_2d - 70 face kp as flat [x,y,score,...] (absolute pixels) indices 0-67: 68 face landmarks index 68: right eye (body[14]) index 69: left eye (body[15]) hand_right_keypoints_2d - 21 right-hand kp (absolute pixels) hand_left_keypoints_2d - 21 left-hand kp (absolute pixels) """ def _flatten(kp_slice, sc_slice): return np.stack([kp_slice[:, 0], kp_slice[:, 1], sc_slice], axis=1).flatten().tolist() frames = [] for img_idx in range(len(all_keypoints)): people = [] for kp_raw, sc_raw in zip(all_keypoints[img_idx], all_scores[img_idx]): kp, sc = _preprocess_keypoints(kp_raw, sc_raw) # 70 face kp = 68 face landmarks + REye (body[14]) + LEye (body[15]) face_kp = np.concatenate([kp[24:92], kp[[14, 15]]], axis=0) face_sc = np.concatenate([sc[24:92], sc[[14, 15]]], axis=0) people.append({ "pose_keypoints_2d": _flatten(kp[0:18], sc[0:18]), "foot_keypoints_2d": _flatten(kp[18:24], sc[18:24]), "face_keypoints_2d": _flatten(face_kp, face_sc), "hand_right_keypoints_2d": _flatten(kp[92:113], sc[92:113]), "hand_left_keypoints_2d": _flatten(kp[113:134], sc[113:134]), }) frames.append({"canvas_width": width, "canvas_height": height, "people": people}) return frames class SDPoseDrawKeypoints(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="SDPoseDrawKeypoints", display_name="SDPose Draw Keypoints", category="image/detection", search_aliases=["openpose", "pose detection", "preprocessor", "keypoints", "pose"], inputs=[ io.Custom("POSE_KEYPOINT").Input("keypoints"), io.Boolean.Input("draw_body", default=True), io.Boolean.Input("draw_hands", default=True), io.Boolean.Input("draw_face", default=True), io.Boolean.Input("draw_feet", default=False), io.Int.Input("stick_width", default=4, min=1, max=10, step=1), io.Int.Input("face_point_size", default=3, min=1, max=10, step=1), io.Float.Input("score_threshold", default=0.3, min=0.0, max=1.0, step=0.01), io.Boolean.Input("draw_head", default=True), ], outputs=[ io.Image.Output(), ], ) @classmethod def execute(cls, keypoints, draw_body, draw_hands, draw_face, draw_feet, stick_width, face_point_size, score_threshold, draw_head) -> io.NodeOutput: if not keypoints: return io.NodeOutput(torch.zeros((1, 64, 64, 3), dtype=torch.float32)) height = keypoints[0]["canvas_height"] width = keypoints[0]["canvas_width"] def _parse(flat, n): arr = np.array(flat, dtype=np.float32).reshape(n, 3) return arr[:, :2], arr[:, 2] def _zeros(n): return np.zeros((n, 2), dtype=np.float32), np.zeros(n, dtype=np.float32) pose_outputs = [] drawer = KeypointDraw() for frame in tqdm(keypoints, desc="Drawing keypoints on frames"): canvas = np.zeros((height, width, 3), dtype=np.uint8) for person in frame["people"]: body_kp, body_sc = _parse(person["pose_keypoints_2d"], 18) foot_raw = person.get("foot_keypoints_2d") foot_kp, foot_sc = _parse(foot_raw, 6) if foot_raw else _zeros(6) face_kp, face_sc = _parse(person["face_keypoints_2d"], 70) face_kp, face_sc = face_kp[:68], face_sc[:68] # drop appended eye kp; body already draws them rhand_kp, rhand_sc = _parse(person["hand_right_keypoints_2d"], 21) lhand_kp, lhand_sc = _parse(person["hand_left_keypoints_2d"], 21) kp = np.concatenate([body_kp, foot_kp, face_kp, rhand_kp, lhand_kp], axis=0) sc = np.concatenate([body_sc, foot_sc, face_sc, rhand_sc, lhand_sc], axis=0) canvas = drawer.draw_wholebody_keypoints( canvas, kp, sc, threshold=score_threshold, draw_body=draw_body, draw_head=draw_head, draw_feet=draw_feet, draw_face=draw_face, draw_hands=draw_hands, stick_width=stick_width, face_point_size=face_point_size, ) pose_outputs.append(canvas) pose_outputs_np = np.stack(pose_outputs) if len(pose_outputs) > 1 else np.expand_dims(pose_outputs[0], 0) final_pose_output = torch.from_numpy(pose_outputs_np).to( device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) / 255.0 return io.NodeOutput(final_pose_output) class SDPoseKeypointExtractor(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="SDPoseKeypointExtractor", display_name="SDPose Keypoint Extractor", category="image/detection", search_aliases=["openpose", "pose detection", "preprocessor", "keypoints", "sdpose"], description="Extract pose keypoints from images using the SDPose model: https://huggingface.co/Comfy-Org/SDPose/tree/main/checkpoints", inputs=[ io.Model.Input("model"), io.Vae.Input("vae"), io.Image.Input("image"), io.Int.Input("batch_size", default=16, min=1, max=10000, step=1), io.BoundingBox.Input("bboxes", optional=True, force_input=True, tooltip="Optional bounding boxes for more accurate detections. Required for multi-person detection."), ], outputs=[ io.Custom("POSE_KEYPOINT").Output("keypoints", tooltip="Keypoints in OpenPose frame format (canvas_width, canvas_height, people)"), ], ) @classmethod def execute(cls, model, vae, image, batch_size, bboxes=None) -> io.NodeOutput: height, width = image.shape[-3], image.shape[-2] context = LotusConditioning().execute().result[0] # Use output_block_patch to capture the last 640-channel feature def output_patch(h, hsp, transformer_options): nonlocal captured_feat if h.shape[1] == 640: # Capture the features for wholebody captured_feat = h.clone() return h, hsp model_clone = model.clone() model_clone.model_options["transformer_options"] = {"patches": {"output_block_patch": [output_patch]}} if not hasattr(model.model.diffusion_model, 'heatmap_head'): raise ValueError("The provided model does not have a heatmap_head. Please use SDPose model from here https://huggingface.co/Comfy-Org/SDPose/tree/main/checkpoints.") head = model.model.diffusion_model.heatmap_head total_images = image.shape[0] captured_feat = None model_w = int(head.heatmap_size[0]) * 4 # 192 * 4 = 768 model_h = int(head.heatmap_size[1]) * 4 # 256 * 4 = 1024 def _resize_to_model(imgs): """Stretch BHWC images to (model_h, model_w), model expects no aspect preservation.""" h, w = imgs.shape[-3], imgs.shape[-2] method = "area" if (model_h <= h and model_w <= w) else "bilinear" chw = imgs.permute(0, 3, 1, 2).float() scaled = comfy.utils.common_upscale(chw, model_w, model_h, upscale_method=method, crop="disabled") return scaled.permute(0, 2, 3, 1), model_w / w, model_h / h def _remap_keypoints(kp, scale_x, scale_y, offset_x=0, offset_y=0): """Remap keypoints from model space back to original image space.""" kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32) invalid = kp[..., 0] < 0 kp[..., 0] = kp[..., 0] / scale_x + offset_x kp[..., 1] = kp[..., 1] / scale_y + offset_y kp[invalid] = -1 return kp def _run_on_latent(latent_batch): """Run one forward pass and return (keypoints_list, scores_list) for the batch.""" nonlocal captured_feat captured_feat = None _ = comfy.sample.sample( model_clone, noise=torch.zeros_like(latent_batch), steps=1, cfg=1.0, sampler_name="euler", scheduler="simple", positive=context, negative=context, latent_image=latent_batch, disable_noise=True, disable_pbar=True, ) return head(captured_feat) # keypoints_batch, scores_batch # all_keypoints / all_scores are lists-of-lists: # outer index = input image index # inner index = detected person (one per bbox, or one for full-image) all_keypoints = [] # shape: [n_images][n_persons] all_scores = [] # shape: [n_images][n_persons] pbar = comfy.utils.ProgressBar(total_images) if bboxes is not None: if not isinstance(bboxes, list): bboxes = [[bboxes]] elif len(bboxes) == 0: bboxes = [None] * total_images # --- bbox-crop mode: one forward pass per crop ------------------------- for img_idx in tqdm(range(total_images), desc="Extracting keypoints from crops"): img = image[img_idx:img_idx + 1] # (1, H, W, C) # Broadcasting: if fewer bbox lists than images, repeat the last one. img_bboxes = bboxes[min(img_idx, len(bboxes) - 1)] if bboxes else None img_keypoints = [] img_scores = [] if img_bboxes: for bbox in img_bboxes: x1 = max(0, int(bbox["x"])) y1 = max(0, int(bbox["y"])) x2 = min(width, int(bbox["x"] + bbox["width"])) y2 = min(height, int(bbox["y"] + bbox["height"])) if x2 <= x1 or y2 <= y1: continue crop = img[:, y1:y2, x1:x2, :] # (1, crop_h, crop_w, C) crop_resized, sx, sy = _resize_to_model(crop) latent_crop = vae.encode(crop_resized) kp_batch, sc_batch = _run_on_latent(latent_crop) kp = _remap_keypoints(kp_batch[0], sx, sy, x1, y1) img_keypoints.append(kp) img_scores.append(sc_batch[0]) else: img_resized, sx, sy = _resize_to_model(img) latent_img = vae.encode(img_resized) kp_batch, sc_batch = _run_on_latent(latent_img) img_keypoints.append(_remap_keypoints(kp_batch[0], sx, sy)) img_scores.append(sc_batch[0]) all_keypoints.append(img_keypoints) all_scores.append(img_scores) pbar.update(1) else: # full-image mode, batched for batch_start in tqdm(range(0, total_images, batch_size), desc="Extracting keypoints"): batch_resized, sx, sy = _resize_to_model(image[batch_start:batch_start + batch_size]) latent_batch = vae.encode(batch_resized) kp_batch, sc_batch = _run_on_latent(latent_batch) for kp, sc in zip(kp_batch, sc_batch): all_keypoints.append([_remap_keypoints(kp, sx, sy)]) all_scores.append([sc]) pbar.update(len(kp_batch)) openpose_frames = _to_openpose_frames(all_keypoints, all_scores, height, width) return io.NodeOutput(openpose_frames) def get_face_bboxes(kp2ds, scale, image_shape): h, w = image_shape kp2ds_face = kp2ds.copy()[1:] * (w, h) min_x, min_y = np.min(kp2ds_face, axis=0) max_x, max_y = np.max(kp2ds_face, axis=0) initial_width = max_x - min_x initial_height = max_y - min_y if initial_width <= 0 or initial_height <= 0: return [0, 0, 0, 0] initial_area = initial_width * initial_height expanded_area = initial_area * scale new_width = np.sqrt(expanded_area * (initial_width / initial_height)) new_height = np.sqrt(expanded_area * (initial_height / initial_width)) delta_width = (new_width - initial_width) / 2 delta_height = (new_height - initial_height) / 4 expanded_min_x = max(min_x - delta_width, 0) expanded_max_x = min(max_x + delta_width, w) expanded_min_y = max(min_y - 3 * delta_height, 0) expanded_max_y = min(max_y + delta_height, h) return [int(expanded_min_x), int(expanded_max_x), int(expanded_min_y), int(expanded_max_y)] class SDPoseFaceBBoxes(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="SDPoseFaceBBoxes", display_name="SDPose Face Bounding Boxes", category="image/detection", search_aliases=["face bbox", "face bounding box", "pose", "keypoints"], inputs=[ io.Custom("POSE_KEYPOINT").Input("keypoints"), io.Float.Input("scale", default=1.5, min=1.0, max=10.0, step=0.1, tooltip="Multiplier for the bounding box area around each detected face."), io.Boolean.Input("force_square", default=True, tooltip="Expand the shorter bbox axis so the crop region is always square."), ], outputs=[ io.BoundingBox.Output("bboxes", tooltip="Face bounding boxes per frame, compatible with SDPoseKeypointExtractor bboxes input."), ], ) @classmethod def execute(cls, keypoints, scale, force_square) -> io.NodeOutput: all_bboxes = [] for frame in keypoints: h = frame["canvas_height"] w = frame["canvas_width"] frame_bboxes = [] for person in frame["people"]: face_flat = person.get("face_keypoints_2d", []) if not face_flat: continue # Parse absolute-pixel face keypoints (70 kp: 68 landmarks + REye + LEye) face_arr = np.array(face_flat, dtype=np.float32).reshape(-1, 3) face_xy = face_arr[:, :2] # (70, 2) in absolute pixels kp_norm = face_xy / np.array([w, h], dtype=np.float32) kp_padded = np.vstack([np.zeros((1, 2), dtype=np.float32), kp_norm]) # (71, 2) x1, x2, y1, y2 = get_face_bboxes(kp_padded, scale, (h, w)) if x2 > x1 and y2 > y1: if force_square: bw, bh = x2 - x1, y2 - y1 if bw != bh: side = max(bw, bh) cx, cy = (x1 + x2) // 2, (y1 + y2) // 2 half = side // 2 x1 = max(0, cx - half) y1 = max(0, cy - half) x2 = min(w, x1 + side) y2 = min(h, y1 + side) # Re-anchor if clamped x1 = max(0, x2 - side) y1 = max(0, y2 - side) frame_bboxes.append({"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1}) all_bboxes.append(frame_bboxes) return io.NodeOutput(all_bboxes) class CropByBBoxes(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="CropByBBoxes", display_name="Crop By Bounding Boxes", category="image/transform", search_aliases=["crop", "face crop", "bbox crop", "pose", "bounding box"], description="Crop and resize regions from the input image batch based on provided bounding boxes.", inputs=[ io.Image.Input("image"), io.BoundingBox.Input("bboxes", force_input=True), io.Int.Input("output_width", default=512, min=64, max=4096, step=8, tooltip="Width each crop is resized to."), io.Int.Input("output_height", default=512, min=64, max=4096, step=8, tooltip="Height each crop is resized to."), io.Int.Input("padding", default=0, min=0, max=1024, step=1, tooltip="Extra padding in pixels added on each side of the bbox before cropping."), io.Combo.Input("keep_aspect", options=["stretch", "pad"], default="stretch", tooltip="Whether to stretch the crop to fit the output size, or pad with black pixels to preserve aspect ratio."), ], outputs=[ io.Image.Output(tooltip="All crops stacked into a single image batch."), ], ) @classmethod def execute(cls, image, bboxes, output_width, output_height, padding, keep_aspect="stretch") -> io.NodeOutput: total_frames = image.shape[0] img_h = image.shape[1] img_w = image.shape[2] num_ch = image.shape[3] if not isinstance(bboxes, list): bboxes = [[bboxes]] elif len(bboxes) == 0: return io.NodeOutput(image) crops = [] for frame_idx in range(total_frames): frame_bboxes = bboxes[min(frame_idx, len(bboxes) - 1)] if not frame_bboxes: continue frame_chw = image[frame_idx].permute(2, 0, 1).unsqueeze(0) # BHWC → BCHW (1, C, H, W) # Union all bboxes for this frame into a single crop region x1 = min(int(b["x"]) for b in frame_bboxes) y1 = min(int(b["y"]) for b in frame_bboxes) x2 = max(int(b["x"] + b["width"]) for b in frame_bboxes) y2 = max(int(b["y"] + b["height"]) for b in frame_bboxes) if padding > 0: x1 = max(0, x1 - padding) y1 = max(0, y1 - padding) x2 = min(img_w, x2 + padding) y2 = min(img_h, y2 + padding) x1, x2 = max(0, x1), min(img_w, x2) y1, y2 = max(0, y1), min(img_h, y2) # Fallback for empty/degenerate crops if x2 <= x1 or y2 <= y1: fallback_size = int(min(img_h, img_w) * 0.3) fb_x1 = max(0, (img_w - fallback_size) // 2) fb_y1 = max(0, int(img_h * 0.1)) fb_x2 = min(img_w, fb_x1 + fallback_size) fb_y2 = min(img_h, fb_y1 + fallback_size) if fb_x2 <= fb_x1 or fb_y2 <= fb_y1: crops.append(torch.zeros(1, num_ch, output_height, output_width, dtype=image.dtype, device=image.device)) continue x1, y1, x2, y2 = fb_x1, fb_y1, fb_x2, fb_y2 crop_chw = frame_chw[:, :, y1:y2, x1:x2] # (1, C, crop_h, crop_w) if keep_aspect == "pad": crop_h, crop_w = y2 - y1, x2 - x1 scale = min(output_width / crop_w, output_height / crop_h) scaled_w = int(round(crop_w * scale)) scaled_h = int(round(crop_h * scale)) scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="area", crop="disabled") pad_left = (output_width - scaled_w) // 2 pad_top = (output_height - scaled_h) // 2 resized = torch.zeros(1, num_ch, output_height, output_width, dtype=image.dtype, device=image.device) resized[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled else: # "stretch" resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="area", crop="disabled") crops.append(resized) if not crops: return io.NodeOutput(image) out_images = torch.cat(crops, dim=0).permute(0, 2, 3, 1) # (N, H, W, C) return io.NodeOutput(out_images) class SDPoseExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: return [ SDPoseKeypointExtractor, SDPoseDrawKeypoints, SDPoseFaceBBoxes, CropByBBoxes, ] async def comfy_entrypoint() -> SDPoseExtension: return SDPoseExtension()