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import os |
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import model_management |
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import torch |
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import comfy.utils |
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import numpy as np |
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import cv2 |
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import math |
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from custom_nodes.facerestore.facelib.utils.face_restoration_helper import FaceRestoreHelper |
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from custom_nodes.facerestore.facelib.detection.retinaface import retinaface |
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from torchvision.transforms.functional import normalize |
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from comfy_extras.chainner_models import model_loading |
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import folder_paths |
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dir_facerestore_models = os.path.join(folder_paths.models_dir, "facerestore_models") |
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dir_facedetection = os.path.join(folder_paths.models_dir, "facedetection") |
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os.makedirs(dir_facerestore_models, exist_ok=True) |
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os.makedirs(dir_facedetection, exist_ok=True) |
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folder_paths.folder_names_and_paths["facerestore_models"] = ([dir_facerestore_models], folder_paths.supported_pt_extensions) |
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def img2tensor(imgs, bgr2rgb=True, float32=True): |
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"""Numpy array to tensor. |
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Args: |
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imgs (list[ndarray] | ndarray): Input images. |
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bgr2rgb (bool): Whether to change bgr to rgb. |
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float32 (bool): Whether to change to float32. |
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Returns: |
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list[tensor] | tensor: Tensor images. If returned results only have |
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one element, just return tensor. |
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""" |
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def _totensor(img, bgr2rgb, float32): |
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if img.shape[2] == 3 and bgr2rgb: |
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if img.dtype == 'float64': |
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img = img.astype('float32') |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
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img = torch.from_numpy(img.transpose(2, 0, 1)) |
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if float32: |
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img = img.float() |
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return img |
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if isinstance(imgs, list): |
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return [_totensor(img, bgr2rgb, float32) for img in imgs] |
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else: |
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return _totensor(imgs, bgr2rgb, float32) |
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def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)): |
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"""Convert torch Tensors into image numpy arrays. |
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After clamping to [min, max], values will be normalized to [0, 1]. |
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Args: |
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tensor (Tensor or list[Tensor]): Accept shapes: |
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1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); |
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2) 3D Tensor of shape (3/1 x H x W); |
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3) 2D Tensor of shape (H x W). |
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Tensor channel should be in RGB order. |
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rgb2bgr (bool): Whether to change rgb to bgr. |
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out_type (numpy type): output types. If ``np.uint8``, transform outputs |
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to uint8 type with range [0, 255]; otherwise, float type with |
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range [0, 1]. Default: ``np.uint8``. |
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min_max (tuple[int]): min and max values for clamp. |
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Returns: |
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(Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of |
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shape (H x W). The channel order is BGR. |
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""" |
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if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))): |
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raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}') |
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if torch.is_tensor(tensor): |
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tensor = [tensor] |
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result = [] |
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for _tensor in tensor: |
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_tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max) |
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_tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0]) |
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n_dim = _tensor.dim() |
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if n_dim == 4: |
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img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy() |
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img_np = img_np.transpose(1, 2, 0) |
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if rgb2bgr: |
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img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) |
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elif n_dim == 3: |
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img_np = _tensor.numpy() |
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img_np = img_np.transpose(1, 2, 0) |
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if img_np.shape[2] == 1: |
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img_np = np.squeeze(img_np, axis=2) |
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else: |
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if rgb2bgr: |
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img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) |
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elif n_dim == 2: |
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img_np = _tensor.numpy() |
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else: |
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raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}') |
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if out_type == np.uint8: |
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img_np = (img_np * 255.0).round() |
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img_np = img_np.astype(out_type) |
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result.append(img_np) |
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if len(result) == 1: |
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result = result[0] |
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return result |
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class FaceRestoreWithModel: |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": { "facerestore_model": ("FACERESTORE_MODEL",), |
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"image": ("IMAGE",), |
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"facedetection": (["retinaface_resnet50", "retinaface_mobile0.25", "YOLOv5l", "YOLOv5n"],) |
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}} |
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RETURN_TYPES = ("IMAGE",) |
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FUNCTION = "restore_face" |
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CATEGORY = "facerestore" |
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def __init__(self): |
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self.face_helper = None |
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def restore_face(self, facerestore_model, image, facedetection): |
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device = model_management.get_torch_device() |
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facerestore_model.to(device) |
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if self.face_helper is None: |
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self.face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model=facedetection, save_ext='png', use_parse=True, device=device) |
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image_np = 255. * image.cpu().numpy() |
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total_images = image_np.shape[0] |
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out_images = np.ndarray(shape=image_np.shape) |
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for i in range(total_images): |
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cur_image_np = image_np[i,:, :, ::-1] |
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original_resolution = cur_image_np.shape[0:2] |
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if facerestore_model is None or self.face_helper is None: |
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return image |
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self.face_helper.clean_all() |
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self.face_helper.read_image(cur_image_np) |
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self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) |
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self.face_helper.align_warp_face() |
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restored_face = None |
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for idx, cropped_face in enumerate(self.face_helper.cropped_faces): |
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cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) |
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normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) |
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cropped_face_t = cropped_face_t.unsqueeze(0).to(device) |
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try: |
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with torch.no_grad(): |
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output = facerestore_model(cropped_face_t)[0] |
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restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) |
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del output |
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torch.cuda.empty_cache() |
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except Exception as error: |
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print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr) |
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restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) |
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restored_face = restored_face.astype('uint8') |
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self.face_helper.add_restored_face(restored_face) |
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self.face_helper.get_inverse_affine(None) |
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restored_img = self.face_helper.paste_faces_to_input_image() |
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restored_img = restored_img[:, :, ::-1] |
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if original_resolution != restored_img.shape[0:2]: |
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restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR) |
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self.face_helper.clean_all() |
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out_images[i] = restored_img |
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restored_img_np = np.array(out_images).astype(np.float32) / 255.0 |
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restored_img_tensor = torch.from_numpy(restored_img_np) |
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return (restored_img_tensor,) |
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class CropFace: |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": { "image": ("IMAGE",), |
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"facedetection": (["retinaface_resnet50", "retinaface_mobile0.25", "YOLOv5l", "YOLOv5n"],) |
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}} |
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RETURN_TYPES = ("IMAGE",) |
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FUNCTION = "crop_face" |
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CATEGORY = "facerestore" |
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def __init__(self): |
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self.face_helper = None |
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def crop_face(self, image, facedetection): |
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device = model_management.get_torch_device() |
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if self.face_helper is None: |
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self.face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model=facedetection, save_ext='png', use_parse=True, device=device) |
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image_np = 255. * image.cpu().numpy() |
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total_images = image_np.shape[0] |
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out_images = np.ndarray(shape=(total_images, 512, 512, 3)) |
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next_idx = 0 |
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for i in range(total_images): |
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cur_image_np = image_np[i,:, :, ::-1] |
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original_resolution = cur_image_np.shape[0:2] |
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if self.face_helper is None: |
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return image |
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self.face_helper.clean_all() |
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self.face_helper.read_image(cur_image_np) |
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self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) |
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self.face_helper.align_warp_face() |
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faces_found = len(self.face_helper.cropped_faces) |
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if faces_found == 0: |
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next_idx += 1 |
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if out_images.shape[0] < next_idx + faces_found: |
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print(out_images.shape) |
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print((next_idx + faces_found, 512, 512, 3)) |
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print('aaaaa') |
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out_images = np.resize(out_images, (next_idx + faces_found, 512, 512, 3)) |
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print(out_images.shape) |
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for j in range(faces_found): |
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cropped_face_1 = self.face_helper.cropped_faces[j] |
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cropped_face_2 = img2tensor(cropped_face_1 / 255., bgr2rgb=True, float32=True) |
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normalize(cropped_face_2, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) |
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cropped_face_3 = cropped_face_2.unsqueeze(0).to(device) |
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cropped_face_4 = tensor2img(cropped_face_3, rgb2bgr=True, min_max=(-1, 1)).astype('uint8') |
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cropped_face_5 = cv2.cvtColor(cropped_face_4, cv2.COLOR_BGR2RGB) |
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out_images[next_idx] = cropped_face_5 |
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next_idx += 1 |
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cropped_face_6 = np.array(out_images).astype(np.float32) / 255.0 |
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cropped_face_7 = torch.from_numpy(cropped_face_6) |
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return (cropped_face_7,) |
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class FaceRestoreModelLoader: |
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@classmethod |
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def INPUT_TYPES(s): |
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return {"required": { "model_name": (folder_paths.get_filename_list("facerestore_models"), ), |
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}} |
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RETURN_TYPES = ("FACERESTORE_MODEL",) |
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FUNCTION = "load_model" |
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CATEGORY = "facerestore" |
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def load_model(self, model_name): |
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model_path = folder_paths.get_full_path("facerestore_models", model_name) |
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sd = comfy.utils.load_torch_file(model_path, safe_load=True) |
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out = model_loading.load_state_dict(sd).eval() |
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return (out, ) |
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NODE_CLASS_MAPPINGS = { |
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"FaceRestoreWithModel": FaceRestoreWithModel, |
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"CropFace": CropFace, |
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"FaceRestoreModelLoader": FaceRestoreModelLoader, |
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} |
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