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from __future__ import annotations |
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import logging |
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import os |
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import torch |
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from modules import ( |
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devices, |
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errors, |
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face_restoration, |
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face_restoration_utils, |
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modelloader, |
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shared, |
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) |
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logger = logging.getLogger(__name__) |
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model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth" |
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model_download_name = "GFPGANv1.4.pth" |
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gfpgan_face_restorer: face_restoration.FaceRestoration | None = None |
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class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration): |
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def name(self): |
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return "GFPGAN" |
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def get_device(self): |
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return devices.device_gfpgan |
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def load_net(self) -> torch.Module: |
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for model_path in modelloader.load_models( |
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model_path=self.model_path, |
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model_url=model_url, |
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command_path=self.model_path, |
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download_name=model_download_name, |
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ext_filter=['.pth'], |
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): |
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if 'GFPGAN' in os.path.basename(model_path): |
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model = modelloader.load_spandrel_model( |
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model_path, |
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device=self.get_device(), |
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expected_architecture='GFPGAN', |
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).model |
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model.different_w = True |
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return model |
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raise ValueError("No GFPGAN model found") |
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def restore(self, np_image): |
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def restore_face(cropped_face_t): |
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assert self.net is not None |
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return self.net(cropped_face_t, return_rgb=False)[0] |
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return self.restore_with_helper(np_image, restore_face) |
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def gfpgan_fix_faces(np_image): |
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if gfpgan_face_restorer: |
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return gfpgan_face_restorer.restore(np_image) |
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logger.warning("GFPGAN face restorer not set up") |
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return np_image |
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def setup_model(dirname: str) -> None: |
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global gfpgan_face_restorer |
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try: |
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face_restoration_utils.patch_facexlib(dirname) |
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gfpgan_face_restorer = FaceRestorerGFPGAN(model_path=dirname) |
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shared.face_restorers.append(gfpgan_face_restorer) |
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except Exception: |
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errors.report("Error setting up GFPGAN", exc_info=True) |
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