125 lines
5.1 KiB
Python
125 lines
5.1 KiB
Python
import os
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import sys
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import traceback
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import cv2
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import torch
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from modules import shared, devices
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from modules.paths import script_path
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import modules.shared
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import modules.face_restoration
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from importlib import reload
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# codeformer people made a choice to include modified basicsr librry to their projectwhich makes
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# it utterly impossiblr to use it alongside with other libraries that also use basicsr, like GFPGAN.
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# I am making a choice to include some files from codeformer to work around this issue.
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pretrain_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
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have_codeformer = False
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codeformer = None
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def setup_codeformer():
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path = modules.paths.paths.get("CodeFormer", None)
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if path is None:
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return
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# both GFPGAN and CodeFormer use bascisr, one has it installed from pip the other uses its own
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#stored_sys_path = sys.path
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#sys.path = [path] + sys.path
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try:
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from torchvision.transforms.functional import normalize
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from modules.codeformer.codeformer_arch import CodeFormer
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from basicsr.utils.download_util import load_file_from_url
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from basicsr.utils import imwrite, img2tensor, tensor2img
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from facelib.utils.face_restoration_helper import FaceRestoreHelper
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from modules.shared import cmd_opts
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net_class = CodeFormer
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class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
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def name(self):
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return "CodeFormer"
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def __init__(self):
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self.net = None
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self.face_helper = None
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def create_models(self):
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if self.net is not None and self.face_helper is not None:
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self.net.to(shared.device)
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return self.net, self.face_helper
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net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(shared.device)
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ckpt_path = load_file_from_url(url=pretrain_model_url, model_dir=os.path.join(path, 'weights/CodeFormer'), progress=True)
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checkpoint = torch.load(ckpt_path)['params_ema']
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net.load_state_dict(checkpoint)
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net.eval()
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face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=shared.device)
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self.net = net
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self.face_helper = face_helper
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self.net.to(shared.device)
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return net, face_helper
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def restore(self, np_image, w=None):
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np_image = np_image[:, :, ::-1]
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original_resolution = np_image.shape[0:2]
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self.create_models()
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self.face_helper.clean_all()
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self.face_helper.read_image(np_image)
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self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
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self.face_helper.align_warp_face()
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for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
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cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
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normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
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cropped_face_t = cropped_face_t.unsqueeze(0).to(shared.device)
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try:
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with torch.no_grad():
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output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
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restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
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del output
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torch.cuda.empty_cache()
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except Exception as error:
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print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
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restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
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restored_face = restored_face.astype('uint8')
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self.face_helper.add_restored_face(restored_face)
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self.face_helper.get_inverse_affine(None)
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restored_img = self.face_helper.paste_faces_to_input_image()
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restored_img = restored_img[:, :, ::-1]
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if original_resolution != restored_img.shape[0:2]:
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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)
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if shared.opts.face_restoration_unload:
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self.net.to(devices.cpu)
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return restored_img
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global have_codeformer
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have_codeformer = True
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global codeformer
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codeformer = FaceRestorerCodeFormer()
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shared.face_restorers.append(codeformer)
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except Exception:
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print("Error setting up CodeFormer:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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# sys.path = stored_sys_path
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