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import os
import model_management
import torch
import comfy.utils
import numpy as np
import cv2
import math
from custom_nodes.facerestore.facelib.utils.face_restoration_helper import FaceRestoreHelper
from custom_nodes.facerestore.facelib.detection.retinaface import retinaface
from torchvision.transforms.functional import normalize
from comfy_extras.chainner_models import model_loading
import folder_paths
dir_facerestore_models = os.path.join(folder_paths.models_dir, "facerestore_models")
dir_facedetection = os.path.join(folder_paths.models_dir, "facedetection")
os.makedirs(dir_facerestore_models, exist_ok=True)
os.makedirs(dir_facedetection, exist_ok=True)
folder_paths.folder_names_and_paths["facerestore_models"] = ([dir_facerestore_models], folder_paths.supported_pt_extensions)
def img2tensor(imgs, bgr2rgb=True, float32=True):
"""Numpy array to tensor.
Args:
imgs (list[ndarray] | ndarray): Input images.
bgr2rgb (bool): Whether to change bgr to rgb.
float32 (bool): Whether to change to float32.
Returns:
list[tensor] | tensor: Tensor images. If returned results only have
one element, just return tensor.
"""
def _totensor(img, bgr2rgb, float32):
if img.shape[2] == 3 and bgr2rgb:
if img.dtype == 'float64':
img = img.astype('float32')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = torch.from_numpy(img.transpose(2, 0, 1))
if float32:
img = img.float()
return img
if isinstance(imgs, list):
return [_totensor(img, bgr2rgb, float32) for img in imgs]
else:
return _totensor(imgs, bgr2rgb, float32)
def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
"""Convert torch Tensors into image numpy arrays.
After clamping to [min, max], values will be normalized to [0, 1].
Args:
tensor (Tensor or list[Tensor]): Accept shapes:
1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
2) 3D Tensor of shape (3/1 x H x W);
3) 2D Tensor of shape (H x W).
Tensor channel should be in RGB order.
rgb2bgr (bool): Whether to change rgb to bgr.
out_type (numpy type): output types. If ``np.uint8``, transform outputs
to uint8 type with range [0, 255]; otherwise, float type with
range [0, 1]. Default: ``np.uint8``.
min_max (tuple[int]): min and max values for clamp.
Returns:
(Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
shape (H x W). The channel order is BGR.
"""
if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
if torch.is_tensor(tensor):
tensor = [tensor]
result = []
for _tensor in tensor:
_tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
_tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
n_dim = _tensor.dim()
if n_dim == 4:
img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
img_np = img_np.transpose(1, 2, 0)
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 3:
img_np = _tensor.numpy()
img_np = img_np.transpose(1, 2, 0)
if img_np.shape[2] == 1: # gray image
img_np = np.squeeze(img_np, axis=2)
else:
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 2:
img_np = _tensor.numpy()
else:
raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}')
if out_type == np.uint8:
# Unlike MATLAB, numpy.unit8() WILL NOT round by default.
img_np = (img_np * 255.0).round()
img_np = img_np.astype(out_type)
result.append(img_np)
if len(result) == 1:
result = result[0]
return result
class FaceRestoreWithModel:
@classmethod
def INPUT_TYPES(s):
return {"required": { "facerestore_model": ("FACERESTORE_MODEL",),
"image": ("IMAGE",),
"facedetection": (["retinaface_resnet50", "retinaface_mobile0.25", "YOLOv5l", "YOLOv5n"],)
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "restore_face"
CATEGORY = "facerestore"
def __init__(self):
self.face_helper = None
def restore_face(self, facerestore_model, image, facedetection):
device = model_management.get_torch_device()
facerestore_model.to(device)
if self.face_helper is None:
self.face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model=facedetection, save_ext='png', use_parse=True, device=device)
image_np = 255. * image.cpu().numpy()
total_images = image_np.shape[0]
out_images = np.ndarray(shape=image_np.shape)
for i in range(total_images):
cur_image_np = image_np[i,:, :, ::-1]
original_resolution = cur_image_np.shape[0:2]
if facerestore_model is None or self.face_helper is None:
return image
self.face_helper.clean_all()
self.face_helper.read_image(cur_image_np)
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
self.face_helper.align_warp_face()
restored_face = None
for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
try:
with torch.no_grad():
#output = facerestore_model(cropped_face_t, w=strength, adain=True)[0]
output = facerestore_model(cropped_face_t)[0]
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
del output
torch.cuda.empty_cache()
except Exception as error:
print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
restored_face = restored_face.astype('uint8')
self.face_helper.add_restored_face(restored_face)
self.face_helper.get_inverse_affine(None)
restored_img = self.face_helper.paste_faces_to_input_image()
restored_img = restored_img[:, :, ::-1]
if original_resolution != restored_img.shape[0:2]:
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)
self.face_helper.clean_all()
# restored_img = cv2.cvtColor(restored_face, cv2.COLOR_BGR2RGB)
out_images[i] = restored_img
restored_img_np = np.array(out_images).astype(np.float32) / 255.0
restored_img_tensor = torch.from_numpy(restored_img_np)
return (restored_img_tensor,)
class CropFace:
@classmethod
def INPUT_TYPES(s):
return {"required": { "image": ("IMAGE",),
"facedetection": (["retinaface_resnet50", "retinaface_mobile0.25", "YOLOv5l", "YOLOv5n"],)
}}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "crop_face"
CATEGORY = "facerestore"
def __init__(self):
self.face_helper = None
def crop_face(self, image, facedetection):
device = model_management.get_torch_device()
if self.face_helper is None:
self.face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model=facedetection, save_ext='png', use_parse=True, device=device)
image_np = 255. * image.cpu().numpy()
total_images = image_np.shape[0]
out_images = np.ndarray(shape=(total_images, 512, 512, 3))
next_idx = 0
for i in range(total_images):
cur_image_np = image_np[i,:, :, ::-1]
original_resolution = cur_image_np.shape[0:2]
if self.face_helper is None:
return image
self.face_helper.clean_all()
self.face_helper.read_image(cur_image_np)
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
self.face_helper.align_warp_face()
faces_found = len(self.face_helper.cropped_faces)
if faces_found == 0:
next_idx += 1 # output black image for no face
if out_images.shape[0] < next_idx + faces_found:
print(out_images.shape)
print((next_idx + faces_found, 512, 512, 3))
print('aaaaa')
out_images = np.resize(out_images, (next_idx + faces_found, 512, 512, 3))
print(out_images.shape)
for j in range(faces_found):
cropped_face_1 = self.face_helper.cropped_faces[j]
cropped_face_2 = img2tensor(cropped_face_1 / 255., bgr2rgb=True, float32=True)
normalize(cropped_face_2, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
cropped_face_3 = cropped_face_2.unsqueeze(0).to(device)
cropped_face_4 = tensor2img(cropped_face_3, rgb2bgr=True, min_max=(-1, 1)).astype('uint8')
cropped_face_5 = cv2.cvtColor(cropped_face_4, cv2.COLOR_BGR2RGB)
out_images[next_idx] = cropped_face_5
next_idx += 1
cropped_face_6 = np.array(out_images).astype(np.float32) / 255.0
cropped_face_7 = torch.from_numpy(cropped_face_6)
return (cropped_face_7,)
class FaceRestoreModelLoader:
@classmethod
def INPUT_TYPES(s):
return {"required": { "model_name": (folder_paths.get_filename_list("facerestore_models"), ),
}}
RETURN_TYPES = ("FACERESTORE_MODEL",)
FUNCTION = "load_model"
CATEGORY = "facerestore"
def load_model(self, model_name):
model_path = folder_paths.get_full_path("facerestore_models", model_name)
sd = comfy.utils.load_torch_file(model_path, safe_load=True)
out = model_loading.load_state_dict(sd).eval()
return (out, )
NODE_CLASS_MAPPINGS = {
"FaceRestoreWithModel": FaceRestoreWithModel,
"CropFace": CropFace,
"FaceRestoreModelLoader": FaceRestoreModelLoader,
}
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