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Diffstat (limited to 'modules/gfpgan_model_arch.py')
| -rw-r--r-- | modules/gfpgan_model_arch.py | 150 |
1 files changed, 0 insertions, 150 deletions
diff --git a/modules/gfpgan_model_arch.py b/modules/gfpgan_model_arch.py deleted file mode 100644 index d81cea96..00000000 --- a/modules/gfpgan_model_arch.py +++ /dev/null @@ -1,150 +0,0 @@ -# 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 |