Spaces:
Runtime error
Runtime error
File size: 6,529 Bytes
6831a54 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
from __future__ import annotations
import logging
import os
from functools import cached_property
from typing import TYPE_CHECKING, Callable
import cv2
import numpy as np
import torch
from modules import devices, errors, face_restoration, shared
from modules_forge.utils import prepare_free_memory
if TYPE_CHECKING:
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
logger = logging.getLogger(__name__)
def bgr_image_to_rgb_tensor(img: np.ndarray) -> torch.Tensor:
"""Convert a BGR NumPy image in [0..1] range to a PyTorch RGB float32 tensor."""
assert img.shape[2] == 3, "image must be RGB"
if img.dtype == "float64":
img = img.astype("float32")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return torch.from_numpy(img.transpose(2, 0, 1)).float()
def rgb_tensor_to_bgr_image(tensor: torch.Tensor, *, min_max=(0.0, 1.0)) -> np.ndarray:
"""
Convert a PyTorch RGB tensor in range `min_max` to a BGR NumPy image in [0..1] range.
"""
tensor = tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])
assert tensor.dim() == 3, "tensor must be RGB"
img_np = tensor.numpy().transpose(1, 2, 0)
if img_np.shape[2] == 1: # gray image, no RGB/BGR required
return np.squeeze(img_np, axis=2)
return cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)
def create_face_helper(device) -> FaceRestoreHelper:
from facexlib.detection import retinaface
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
if hasattr(retinaface, 'device'):
retinaface.device = device
return FaceRestoreHelper(
upscale_factor=1,
face_size=512,
crop_ratio=(1, 1),
det_model='retinaface_resnet50',
save_ext='png',
use_parse=True,
device=device,
)
def restore_with_face_helper(
np_image: np.ndarray,
face_helper: FaceRestoreHelper,
restore_face: Callable[[torch.Tensor], torch.Tensor],
) -> np.ndarray:
"""
Find faces in the image using face_helper, restore them using restore_face, and paste them back into the image.
`restore_face` should take a cropped face image and return a restored face image.
"""
from torchvision.transforms.functional import normalize
np_image = np_image[:, :, ::-1]
original_resolution = np_image.shape[0:2]
try:
logger.debug("Detecting faces...")
face_helper.clean_all()
face_helper.read_image(np_image)
face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
face_helper.align_warp_face()
logger.debug("Found %d faces, restoring", len(face_helper.cropped_faces))
for cropped_face in face_helper.cropped_faces:
cropped_face_t = bgr_image_to_rgb_tensor(cropped_face / 255.0)
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(devices.device_codeformer)
try:
with torch.no_grad():
cropped_face_t = restore_face(cropped_face_t)
devices.torch_gc()
except Exception:
errors.report('Failed face-restoration inference', exc_info=True)
restored_face = rgb_tensor_to_bgr_image(cropped_face_t, min_max=(-1, 1))
restored_face = (restored_face * 255.0).astype('uint8')
face_helper.add_restored_face(restored_face)
logger.debug("Merging restored faces into image")
face_helper.get_inverse_affine(None)
img = face_helper.paste_faces_to_input_image()
img = img[:, :, ::-1]
if original_resolution != img.shape[0:2]:
img = cv2.resize(
img,
(0, 0),
fx=original_resolution[1] / img.shape[1],
fy=original_resolution[0] / img.shape[0],
interpolation=cv2.INTER_LINEAR,
)
logger.debug("Face restoration complete")
finally:
face_helper.clean_all()
return img
class CommonFaceRestoration(face_restoration.FaceRestoration):
net: torch.Module | None
model_url: str
model_download_name: str
def __init__(self, model_path: str):
super().__init__()
self.net = None
self.model_path = model_path
os.makedirs(model_path, exist_ok=True)
@cached_property
def face_helper(self) -> FaceRestoreHelper:
return create_face_helper(self.get_device())
def send_model_to(self, device):
if self.net:
logger.debug("Sending %s to %s", self.net, device)
self.net.to(device)
if self.face_helper:
logger.debug("Sending face helper to %s", device)
self.face_helper.face_det.to(device)
self.face_helper.face_parse.to(device)
def get_device(self):
raise NotImplementedError("get_device must be implemented by subclasses")
def load_net(self) -> torch.Module:
raise NotImplementedError("load_net must be implemented by subclasses")
def restore_with_helper(
self,
np_image: np.ndarray,
restore_face: Callable[[torch.Tensor], torch.Tensor],
) -> np.ndarray:
try:
if self.net is None:
self.net = self.load_net()
except Exception:
logger.warning("Unable to load face-restoration model", exc_info=True)
return np_image
try:
prepare_free_memory()
self.send_model_to(self.get_device())
return restore_with_face_helper(np_image, self.face_helper, restore_face)
finally:
if shared.opts.face_restoration_unload:
self.send_model_to(devices.cpu)
def patch_facexlib(dirname: str) -> None:
import facexlib.detection
import facexlib.parsing
det_facex_load_file_from_url = facexlib.detection.load_file_from_url
par_facex_load_file_from_url = facexlib.parsing.load_file_from_url
def update_kwargs(kwargs):
return dict(kwargs, save_dir=dirname, model_dir=None)
def facex_load_file_from_url(**kwargs):
return det_facex_load_file_from_url(**update_kwargs(kwargs))
def facex_load_file_from_url2(**kwargs):
return par_facex_load_file_from_url(**update_kwargs(kwargs))
facexlib.detection.load_file_from_url = facex_load_file_from_url
facexlib.parsing.load_file_from_url = facex_load_file_from_url2
|