support 1.1 and use torchscript for dwpose
This commit is contained in:
kijai
@@ -14,7 +14,7 @@
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"type": "MimicMotionGetPoses",
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@@ -406,10 +377,11 @@
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"params": {
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"filename": "MimicMotion_00002.mp4",
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@@ -625,37 +544,15 @@
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"hidden": false,
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"params": {
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"text": ""
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"widgets_values": [
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"Downloads MimicMotion model and fp16 version of SVD XT 1.1\n\nlcm version is experimental and most likely doesn't work well"
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"config": {},
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"ds": {
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@@ -20,7 +20,7 @@ class DWposeDetector:
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device: (str) 'cpu' or 'cuda:{device_id}'
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"""
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def __init__(self, model_det, model_pose, device='cpu'):
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self.pose_estimation = Wholebody(model_det=model_det, model_pose=model_pose, device=device)
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self.pose_estimation = Wholebody(model_det=model_det, model_pose=model_pose)
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def __call__(self, oriImg):
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oriImg = oriImg.copy()
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@@ -0,0 +1,125 @@
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import cv2
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import numpy as np
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import torch
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def nms(boxes, scores, nms_thr):
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"""Single class NMS implemented in Numpy."""
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x1 = boxes[:, 0]
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y1 = boxes[:, 1]
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x2 = boxes[:, 2]
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y2 = boxes[:, 3]
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areas = (x2 - x1 + 1) * (y2 - y1 + 1)
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order = scores.argsort()[::-1]
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keep = []
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while order.size > 0:
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i = order[0]
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keep.append(i)
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xx1 = np.maximum(x1[i], x1[order[1:]])
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yy1 = np.maximum(y1[i], y1[order[1:]])
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xx2 = np.minimum(x2[i], x2[order[1:]])
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yy2 = np.minimum(y2[i], y2[order[1:]])
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= nms_thr)[0]
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order = order[inds + 1]
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return keep
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def multiclass_nms(boxes, scores, nms_thr, score_thr):
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"""Multiclass NMS implemented in Numpy. Class-aware version."""
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final_dets = []
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num_classes = scores.shape[1]
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for cls_ind in range(num_classes):
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cls_scores = scores[:, cls_ind]
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valid_score_mask = cls_scores > score_thr
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if valid_score_mask.sum() == 0:
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continue
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else:
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valid_scores = cls_scores[valid_score_mask]
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valid_boxes = boxes[valid_score_mask]
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keep = nms(valid_boxes, valid_scores, nms_thr)
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if len(keep) > 0:
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cls_inds = np.ones((len(keep), 1)) * cls_ind
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dets = np.concatenate(
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[valid_boxes[keep], valid_scores[keep, None], cls_inds], 1
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)
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final_dets.append(dets)
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if len(final_dets) == 0:
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return None
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return np.concatenate(final_dets, 0)
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def demo_postprocess(outputs, img_size, p6=False):
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grids = []
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expanded_strides = []
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strides = [8, 16, 32] if not p6 else [8, 16, 32, 64]
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hsizes = [img_size[0] // stride for stride in strides]
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wsizes = [img_size[1] // stride for stride in strides]
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for hsize, wsize, stride in zip(hsizes, wsizes, strides):
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xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
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grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
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grids.append(grid)
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shape = grid.shape[:2]
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expanded_strides.append(np.full((*shape, 1), stride))
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grids = np.concatenate(grids, 1)
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expanded_strides = np.concatenate(expanded_strides, 1)
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outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
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outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
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return outputs
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def preprocess(img, input_size, swap=(2, 0, 1)):
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if len(img.shape) == 3:
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padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
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else:
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padded_img = np.ones(input_size, dtype=np.uint8) * 114
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r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
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resized_img = cv2.resize(
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img,
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(int(img.shape[1] * r), int(img.shape[0] * r)),
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interpolation=cv2.INTER_LINEAR,
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).astype(np.uint8)
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padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
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padded_img = padded_img.transpose(swap)
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padded_img = np.ascontiguousarray(padded_img, dtype=np.float32)
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return padded_img, r
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def inference_detector(model, oriImg, detect_classes=[0]):
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input_shape = (640,640)
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img, ratio = preprocess(oriImg, input_shape)
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device, dtype = next(model.parameters()).device, next(model.parameters()).dtype
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input = img[None, :, :, :]
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input = torch.from_numpy(input).to(device, dtype)
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output = model(input).float().cpu().detach().numpy()
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predictions = demo_postprocess(output[0], input_shape)
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boxes = predictions[:, :4]
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scores = predictions[:, 4:5] * predictions[:, 5:]
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||||
boxes_xyxy = np.ones_like(boxes)
|
||||
boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2.
|
||||
boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2.
|
||||
boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2.
|
||||
boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2.
|
||||
boxes_xyxy /= ratio
|
||||
dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
|
||||
if dets is None:
|
||||
return None
|
||||
final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
|
||||
isscore = final_scores>0.3
|
||||
iscat = np.isin(final_cls_inds, detect_classes)
|
||||
isbbox = [ i and j for (i, j) in zip(isscore, iscat)]
|
||||
final_boxes = final_boxes[isbbox]
|
||||
return final_boxes
|
||||
@@ -0,0 +1,363 @@
|
||||
from typing import List, Tuple
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
def preprocess(
|
||||
img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256)
|
||||
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
|
||||
"""Do preprocessing for DWPose 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(model, img, bs=5):
|
||||
"""Inference DWPose model implemented in TorchScript.
|
||||
|
||||
Args:
|
||||
model : TorchScript Model.
|
||||
img : Input image in shape.
|
||||
|
||||
Returns:
|
||||
outputs : Output of DWPose model.
|
||||
"""
|
||||
all_out = []
|
||||
# build input
|
||||
orig_img_count = len(img)
|
||||
#Pad zeros to fit batch size
|
||||
for _ in range(bs - (orig_img_count % bs)):
|
||||
img.append(np.zeros_like(img[0]))
|
||||
input = np.stack(img, axis=0).transpose(0, 3, 1, 2)
|
||||
device, dtype = next(model.parameters()).device, next(model.parameters()).dtype
|
||||
input = torch.from_numpy(input).to(device, dtype)
|
||||
|
||||
out1, out2 = [], []
|
||||
for i in range(input.shape[0] // bs):
|
||||
curr_batch_output = model(input[i*bs:(i+1)*bs])
|
||||
out1.append(curr_batch_output[0].float())
|
||||
out2.append(curr_batch_output[1].float())
|
||||
out1, out2 = torch.cat(out1, dim=0)[:orig_img_count], torch.cat(out2, dim=0)[:orig_img_count]
|
||||
out1, out2 = out1.float().cpu().detach().numpy(), out2.float().cpu().detach().numpy()
|
||||
all_outputs = out1, out2
|
||||
|
||||
for batch_idx in range(len(all_outputs[0])):
|
||||
outputs = [all_outputs[i][batch_idx:batch_idx+1,...] for i in range(len(all_outputs))]
|
||||
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 DWPose 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.) -> 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.),
|
||||
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., src_w * -0.5]), rot_rad)
|
||||
dst_dir = np.array([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.] = -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(model, out_bbox, oriImg, model_input_size=(288, 384)):
|
||||
resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size)
|
||||
#outputs = inference(session, resized_img, dtype)
|
||||
outputs = inference(model, resized_img)
|
||||
|
||||
keypoints, scores = postprocess(outputs, model_input_size, center, scale)
|
||||
|
||||
return keypoints, scores
|
||||
@@ -1,23 +1,30 @@
|
||||
import numpy as np
|
||||
import onnxruntime as ort
|
||||
|
||||
from .onnxdet import inference_detector
|
||||
from .onnxpose import inference_pose
|
||||
import comfy.model_management as mm
|
||||
|
||||
#import onnxruntime as ort
|
||||
# from .onnxdet import inference_detector
|
||||
# from .onnxpose import inference_pose
|
||||
|
||||
from .jit_det import inference_detector as inference_jit_yolox
|
||||
from .jit_pose import inference_pose as inference_jit_pose
|
||||
|
||||
class Wholebody:
|
||||
"""detect human pose by dwpose
|
||||
"""
|
||||
def __init__(self, model_det, model_pose, device="cpu"):
|
||||
providers = ['CPUExecutionProvider'] if device == 'cpu' else ['CUDAExecutionProvider']
|
||||
provider_options = None if device == 'cpu' else [{'device_id': 0}]
|
||||
def __init__(self, model_det, model_pose):
|
||||
#providers = ['CPUExecutionProvider'] if device == 'cpu' else ['CUDAExecutionProvider']
|
||||
#provider_options = None if device == 'cpu' else [{'device_id': 0}]
|
||||
|
||||
self.session_det = ort.InferenceSession(
|
||||
path_or_bytes=model_det, providers=providers, provider_options=provider_options
|
||||
)
|
||||
self.session_pose = ort.InferenceSession(
|
||||
path_or_bytes=model_pose, providers=providers, provider_options=provider_options
|
||||
)
|
||||
# self.session_det = ort.InferenceSession(
|
||||
# path_or_bytes=model_det, providers=providers, provider_options=provider_options
|
||||
# )
|
||||
# self.session_pose = ort.InferenceSession(
|
||||
# path_or_bytes=model_pose, providers=providers, provider_options=provider_options
|
||||
# )
|
||||
|
||||
self.det = model_det
|
||||
self.pose = model_pose
|
||||
|
||||
def __call__(self, oriImg):
|
||||
"""call to process dwpose-detect
|
||||
@@ -26,8 +33,9 @@ class Wholebody:
|
||||
oriImg (np.ndarray): detected image
|
||||
|
||||
"""
|
||||
det_result = inference_detector(self.session_det, oriImg)
|
||||
keypoints, scores = inference_pose(self.session_pose, det_result, oriImg)
|
||||
|
||||
det_result = inference_jit_yolox(self.det, oriImg, detect_classes=[0])
|
||||
keypoints, scores = inference_jit_pose(self.pose, det_result, oriImg)
|
||||
|
||||
keypoints_info = np.concatenate(
|
||||
(keypoints, scores[..., None]), axis=-1)
|
||||
|
||||
@@ -635,10 +635,10 @@ class MimicMotionPipeline(DiffusionPipeline):
|
||||
# Check if the current timestep is within the start and end step range
|
||||
if start_step_index <= i <= end_step_index:
|
||||
# Apply pose_latents as currently done
|
||||
print(f"Applying pose on step {i}")
|
||||
#print(f"Applying pose on step {i}")
|
||||
pose_latents_to_use = pose_latents[:, idx].flatten(0, 1)
|
||||
else:
|
||||
print(f"Not applying pose on step {i}")
|
||||
#print(f"Not applying pose on step {i}")
|
||||
# Apply an alternative if pose_latents should not be used outside this range
|
||||
# This could be zeros, or any other placeholder logic you define.
|
||||
pose_latents_to_use = torch.zeros_like(pose_latents[:, idx].flatten(0, 1))
|
||||
|
||||
@@ -19,6 +19,10 @@ from .mimicmotion.modules.pose_net import PoseNet
|
||||
|
||||
from .lcm_scheduler import AnimateLCMSVDStochasticIterativeScheduler
|
||||
|
||||
from accelerate import init_empty_weights
|
||||
from accelerate.utils import set_module_tensor_to_device
|
||||
|
||||
|
||||
def loglinear_interp(t_steps, num_steps):
|
||||
"""
|
||||
Performs log-linear interpolation of a given array of decreasing numbers.
|
||||
@@ -59,7 +63,8 @@ class DownloadAndLoadMimicMotionModel:
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"model": (
|
||||
[ 'MimicMotion-fp16.safetensors',
|
||||
[ 'MimicMotionMergedUnet_1-0-fp16.safetensors',
|
||||
'MimicMotionMergedUnet_1-1-fp16.safetensors',
|
||||
],
|
||||
),
|
||||
"precision": (
|
||||
@@ -70,8 +75,6 @@ class DownloadAndLoadMimicMotionModel:
|
||||
], {
|
||||
"default": 'fp16'
|
||||
}),
|
||||
"lcm": ("BOOLEAN", {"default": False}),
|
||||
|
||||
},
|
||||
}
|
||||
|
||||
@@ -80,12 +83,12 @@ class DownloadAndLoadMimicMotionModel:
|
||||
FUNCTION = "loadmodel"
|
||||
CATEGORY = "MimicMotionWrapper"
|
||||
|
||||
def loadmodel(self, precision, model, lcm):
|
||||
def loadmodel(self, precision, model):
|
||||
device = mm.get_torch_device()
|
||||
mm.soft_empty_cache()
|
||||
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
|
||||
|
||||
pbar = comfy.utils.ProgressBar(3)
|
||||
pbar = comfy.utils.ProgressBar(5)
|
||||
|
||||
download_path = os.path.join(folder_paths.models_dir, "mimicmotion")
|
||||
model_path = os.path.join(download_path, model)
|
||||
@@ -102,56 +105,52 @@ class DownloadAndLoadMimicMotionModel:
|
||||
pbar.update(1)
|
||||
|
||||
svd_path = os.path.join(folder_paths.models_dir, "diffusers", "stable-video-diffusion-img2vid-xt-1-1")
|
||||
svd_lcm_path = os.path.join(folder_paths.models_dir, "diffusers", "stable-video-diffusion-img2vid-xt-1-1-lcm", "unet_lcm")
|
||||
|
||||
if lcm and not os.path.exists(svd_lcm_path):
|
||||
print(f"Downloading AnimateLCM SVD model to: {model_path}")
|
||||
if not os.path.exists(svd_path):
|
||||
print(f"Downloading SVD model to: {model_path}")
|
||||
from huggingface_hub import snapshot_download
|
||||
snapshot_download(repo_id="Kijai/AnimateLCM-SVD-Comfy",
|
||||
allow_patterns=[f"*.json", "*diffusion_pytorch_model.fp16.safetensors*"],
|
||||
snapshot_download(repo_id="vdo/stable-video-diffusion-img2vid-xt-1-1",
|
||||
allow_patterns=[f"*.json", "*fp16*"],
|
||||
ignore_patterns=["*unet*"],
|
||||
local_dir=svd_path,
|
||||
local_dir_use_symlinks=False)
|
||||
else:
|
||||
if not os.path.exists(svd_path):
|
||||
print(f"Downloading SVD model to: {model_path}")
|
||||
from huggingface_hub import snapshot_download
|
||||
snapshot_download(repo_id="vdo/stable-video-diffusion-img2vid-xt-1-1",
|
||||
allow_patterns=[f"*.json", "*fp16*"],
|
||||
local_dir=svd_path,
|
||||
local_dir_use_symlinks=False)
|
||||
pbar.update(1)
|
||||
|
||||
mimicmotion_models = MimicMotionModel(svd_path, lcm=lcm).to(device=device).eval()
|
||||
mimic_motion_sd = comfy.utils.load_torch_file(model_path)
|
||||
mimicmotion_models.load_state_dict(mimic_motion_sd, strict=False)
|
||||
unet_config = UNetSpatioTemporalConditionModel.load_config(svd_path, subfolder="unet", variant="fp16")
|
||||
print("Loading UNET")
|
||||
with (init_empty_weights()):
|
||||
self.unet = UNetSpatioTemporalConditionModel.from_config(unet_config)
|
||||
sd = comfy.utils.load_torch_file(os.path.join(model_path))
|
||||
for key in sd:
|
||||
set_module_tensor_to_device(self.unet, key, dtype=dtype, device=device, value=sd[key])
|
||||
del sd
|
||||
pbar.update(1)
|
||||
|
||||
if lcm:
|
||||
lcm_noise_scheduler = AnimateLCMSVDStochasticIterativeScheduler(
|
||||
num_train_timesteps=40,
|
||||
sigma_min=0.002,
|
||||
sigma_max=700.0,
|
||||
sigma_data=1.0,
|
||||
s_noise=1.0,
|
||||
rho=7,
|
||||
clip_denoised=False,
|
||||
)
|
||||
scheduler = lcm_noise_scheduler
|
||||
else:
|
||||
scheduler = mimicmotion_models.noise_scheduler
|
||||
print("Loading VAE")
|
||||
self.vae = AutoencoderKLTemporalDecoder.from_pretrained(svd_path, subfolder="vae", variant="fp16", low_cpu_mem_usage=True).to(dtype).to(device).eval()
|
||||
|
||||
pipeline = MimicMotionPipeline(
|
||||
vae = mimicmotion_models.vae,
|
||||
image_encoder = mimicmotion_models.image_encoder,
|
||||
unet = mimicmotion_models.unet,
|
||||
scheduler = scheduler,
|
||||
feature_extractor = mimicmotion_models.feature_extractor,
|
||||
pose_net = mimicmotion_models.pose_net,
|
||||
)
|
||||
print("Loading IMAGE_ENCODER")
|
||||
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(svd_path, subfolder="image_encoder", variant="fp16", low_cpu_mem_usage=True).to(dtype).to(device).eval()
|
||||
pbar.update(1)
|
||||
self.noise_scheduler = EulerDiscreteScheduler.from_pretrained(svd_path, subfolder="scheduler")
|
||||
self.feature_extractor = CLIPImageProcessor.from_pretrained(svd_path, subfolder="feature_extractor")
|
||||
|
||||
pipeline.unet.to(dtype)
|
||||
pipeline.pose_net.to(dtype)
|
||||
pipeline.vae.to(dtype)
|
||||
pipeline.image_encoder.to(dtype)
|
||||
print("Loading POSE_NET")
|
||||
self.pose_net = PoseNet(noise_latent_channels=self.unet.config.block_out_channels[0]).to(dtype).to(device).eval()
|
||||
pose_net_sd = comfy.utils.load_torch_file(os.path.join(script_directory, 'models', 'mimic_motion_pose_net.safetensors'))
|
||||
|
||||
self.unet.load_state_dict(pose_net_sd, strict=False)
|
||||
self.pose_net.load_state_dict(pose_net_sd, strict=False)
|
||||
del pose_net_sd
|
||||
|
||||
pipeline = MimicMotionPipeline(
|
||||
vae = self.vae,
|
||||
image_encoder = self.image_encoder,
|
||||
unet = self.unet,
|
||||
scheduler = self.noise_scheduler,
|
||||
feature_extractor = self.feature_extractor,
|
||||
pose_net = self.pose_net,
|
||||
)
|
||||
|
||||
mimic_model = {
|
||||
'pipeline': pipeline,
|
||||
@@ -266,7 +265,7 @@ class MimicMotionSampler:
|
||||
original_scheduler = pipeline.scheduler
|
||||
|
||||
if optional_scheduler is not None:
|
||||
print("Using optional scheduler: ", optional_scheduler)
|
||||
print("Using optional scheduler: ", optional_scheduler['noise_scheduler'])
|
||||
pipeline.scheduler = optional_scheduler['noise_scheduler']
|
||||
sigmas = optional_scheduler['sigmas']
|
||||
|
||||
@@ -375,13 +374,17 @@ class MimicMotionGetPoses:
|
||||
|
||||
def process(self, ref_image, pose_images, include_body, include_hand, include_face):
|
||||
device = mm.get_torch_device()
|
||||
offload_device = mm.unet_offload_device()
|
||||
from .mimicmotion.dwpose.util import draw_pose
|
||||
from .mimicmotion.dwpose.dwpose_detector import DWposeDetector
|
||||
|
||||
assert ref_image.shape[1:3] == pose_images.shape[1:3], "ref_image and pose_images must have the same resolution"
|
||||
|
||||
yolo_model = "yolox_l.onnx"
|
||||
dw_pose_model = "dw-ll_ucoco_384.onnx"
|
||||
#yolo_model = "yolox_l.onnx"
|
||||
#dw_pose_model = "dw-ll_ucoco_384.onnx"
|
||||
dw_pose_model = "dw-ll_ucoco_384_bs5.torchscript.pt"
|
||||
yolo_model = "yolox_l.torchscript.pt"
|
||||
|
||||
model_base_path = os.path.join(script_directory, "models", "DWPose")
|
||||
|
||||
model_det=os.path.join(model_base_path, yolo_model)
|
||||
@@ -390,7 +393,7 @@ class MimicMotionGetPoses:
|
||||
if not os.path.exists(model_det):
|
||||
print(f"Downloading yolo model to: {model_base_path}")
|
||||
from huggingface_hub import snapshot_download
|
||||
snapshot_download(repo_id="yzd-v/DWPose",
|
||||
snapshot_download(repo_id="hr16/yolox-onnx",
|
||||
allow_patterns=[f"*{yolo_model}*"],
|
||||
local_dir=model_base_path,
|
||||
local_dir_use_symlinks=False)
|
||||
@@ -398,23 +401,34 @@ class MimicMotionGetPoses:
|
||||
if not os.path.exists(model_pose):
|
||||
print(f"Downloading dwpose model to: {model_base_path}")
|
||||
from huggingface_hub import snapshot_download
|
||||
snapshot_download(repo_id="yzd-v/DWPose",
|
||||
snapshot_download(repo_id="hr16/DWPose-TorchScript-BatchSize5",
|
||||
allow_patterns=[f"*{dw_pose_model}*"],
|
||||
local_dir=model_base_path,
|
||||
local_dir_use_symlinks=False)
|
||||
|
||||
model_det=os.path.join(model_base_path, yolo_model)
|
||||
model_pose=os.path.join(model_base_path, dw_pose_model)
|
||||
|
||||
dwprocessor = DWposeDetector(
|
||||
model_det=os.path.join(model_base_path, "yolox_l.onnx"),
|
||||
model_pose=os.path.join(model_base_path, "dw-ll_ucoco_384.onnx"),
|
||||
device=device)
|
||||
if not hasattr(self, "det") or not hasattr(self, "pose"):
|
||||
self.det = torch.jit.load(model_det)
|
||||
self.pose = torch.jit.load(model_pose)
|
||||
|
||||
self.dwprocessor = DWposeDetector(
|
||||
model_det=self.det,
|
||||
model_pose=self.pose)
|
||||
|
||||
ref_image = ref_image.squeeze(0).cpu().numpy() * 255
|
||||
|
||||
self.det = self.det.to(device)
|
||||
self.pose = self.pose.to(device)
|
||||
|
||||
# select ref-keypoint from reference pose for pose rescale
|
||||
ref_pose = dwprocessor(ref_image)
|
||||
ref_keypoint_id = [0, 1, 2, 5, 8, 11, 14, 15, 16, 17]
|
||||
ref_pose = self.dwprocessor(ref_image)
|
||||
#ref_keypoint_id = [0, 1, 2, 5, 8, 11, 14, 15, 16, 17]
|
||||
ref_keypoint_id = [0, 1, 2, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
|
||||
ref_keypoint_id = [i for i in ref_keypoint_id \
|
||||
if ref_pose['bodies']['score'].shape[0] > 0 and ref_pose['bodies']['score'][0][i] > 0.3]
|
||||
#if ref_pose['bodies']['score'].shape[0] > 0 and ref_pose['bodies']['score'][0][i] > 0.3]
|
||||
if len(ref_pose['bodies']['subset']) > 0 and ref_pose['bodies']['subset'][0][i] >= .0]
|
||||
ref_body = ref_pose['bodies']['candidate'][ref_keypoint_id]
|
||||
|
||||
height, width, _ = ref_image.shape
|
||||
@@ -424,9 +438,12 @@ class MimicMotionGetPoses:
|
||||
pbar = comfy.utils.ProgressBar(len(pose_images_np))
|
||||
detected_poses_np_list = []
|
||||
for img_np in pose_images_np:
|
||||
detected_poses_np_list.append(dwprocessor(img_np))
|
||||
detected_poses_np_list.append(self.dwprocessor(img_np))
|
||||
pbar.update(1)
|
||||
|
||||
self.det = self.det.to(offload_device)
|
||||
self.pose = self.pose.to(offload_device)
|
||||
|
||||
detected_bodies = np.stack(
|
||||
[p['bodies']['candidate'] for p in detected_poses_np_list if p['bodies']['candidate'].shape[0] == 18])[:,
|
||||
ref_keypoint_id]
|
||||
|
||||
Reference in New Issue
Block a user