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Diffstat (limited to 'modules/gfpgan_model_arch.py')
| -rw-r--r-- | modules/gfpgan_model_arch.py | 150 |
1 files changed, 150 insertions, 0 deletions
diff --git a/modules/gfpgan_model_arch.py b/modules/gfpgan_model_arch.py new file mode 100644 index 00000000..d81cea96 --- /dev/null +++ b/modules/gfpgan_model_arch.py @@ -0,0 +1,150 @@ +# GFPGAN likes to download stuff "wherever", and we're trying to fix that, so this is a copy of the original... + +import cv2 +import os +import torch +from basicsr.utils import img2tensor, tensor2img +from basicsr.utils.download_util import load_file_from_url +from facexlib.utils.face_restoration_helper import FaceRestoreHelper +from torchvision.transforms.functional import normalize + +from gfpgan.archs.gfpgan_bilinear_arch import GFPGANBilinear +from gfpgan.archs.gfpganv1_arch import GFPGANv1 +from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean + +ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + + +class GFPGANerr(): + """Helper for restoration with GFPGAN. + + It will detect and crop faces, and then resize the faces to 512x512. + GFPGAN is used to restored the resized faces. + The background is upsampled with the bg_upsampler. + Finally, the faces will be pasted back to the upsample background image. + + Args: + model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically). + upscale (float): The upscale of the final output. Default: 2. + arch (str): The GFPGAN architecture. Option: clean | original. Default: clean. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + bg_upsampler (nn.Module): The upsampler for the background. Default: None. + """ + + def __init__(self, model_path, model_dir, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None, device=None): + self.upscale = upscale + self.bg_upsampler = bg_upsampler + + # initialize model + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device + # initialize the GFP-GAN + if arch == 'clean': + self.gfpgan = GFPGANv1Clean( + out_size=512, + num_style_feat=512, + channel_multiplier=channel_multiplier, + decoder_load_path=None, + fix_decoder=False, + num_mlp=8, + input_is_latent=True, + different_w=True, + narrow=1, + sft_half=True) + elif arch == 'bilinear': + self.gfpgan = GFPGANBilinear( + out_size=512, + num_style_feat=512, + channel_multiplier=channel_multiplier, + decoder_load_path=None, + fix_decoder=False, + num_mlp=8, + input_is_latent=True, + different_w=True, + narrow=1, + sft_half=True) + elif arch == 'original': + self.gfpgan = GFPGANv1( + out_size=512, + num_style_feat=512, + channel_multiplier=channel_multiplier, + decoder_load_path=None, + fix_decoder=True, + num_mlp=8, + input_is_latent=True, + different_w=True, + narrow=1, + sft_half=True) + elif arch == 'RestoreFormer': + from gfpgan.archs.restoreformer_arch import RestoreFormer + self.gfpgan = RestoreFormer() + # initialize face helper + self.face_helper = FaceRestoreHelper( + upscale, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + save_ext='png', + use_parse=True, + device=self.device, + model_rootpath=model_dir) + + if model_path.startswith('https://'): + model_path = load_file_from_url( + url=model_path, model_dir=model_dir, progress=True, file_name=None) + loadnet = torch.load(model_path) + if 'params_ema' in loadnet: + keyname = 'params_ema' + else: + keyname = 'params' + self.gfpgan.load_state_dict(loadnet[keyname], strict=True) + self.gfpgan.eval() + self.gfpgan = self.gfpgan.to(self.device) + + @torch.no_grad() + def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True, weight=0.5): + self.face_helper.clean_all() + + if has_aligned: # the inputs are already aligned + img = cv2.resize(img, (512, 512)) + self.face_helper.cropped_faces = [img] + else: + self.face_helper.read_image(img) + # get face landmarks for each face + self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5) + # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels + # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations. + # align and warp each face + self.face_helper.align_warp_face() + + # face restoration + for cropped_face in self.face_helper.cropped_faces: + # prepare data + 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(self.device) + + try: + output = self.gfpgan(cropped_face_t, return_rgb=False, weight=weight)[0] + # convert to image + restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)) + except RuntimeError as error: + print(f'\tFailed inference for GFPGAN: {error}.') + restored_face = cropped_face + + restored_face = restored_face.astype('uint8') + self.face_helper.add_restored_face(restored_face) + + if not has_aligned and paste_back: + # upsample the background + if self.bg_upsampler is not None: + # Now only support RealESRGAN for upsampling background + bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0] + else: + bg_img = None + + self.face_helper.get_inverse_affine(None) + # paste each restored face to the input image + restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img) + return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img + else: + return self.face_helper.cropped_faces, self.face_helper.restored_faces, None |