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