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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
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:
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
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