f4a488f585
* FaceXLib/FaceLib doesn't pass the device argument to RetinaFace but instead chooses one itself and sets it to a global - in order to use a device other than its internally chosen default it is necessary to manually replace the default value * The GFPGAN constructor needs the device argument to work with MPS or a CUDA device ID that differs from the default
117 lines
3.8 KiB
Python
117 lines
3.8 KiB
Python
import os
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import sys
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import traceback
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import facexlib
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import gfpgan
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import modules.face_restoration
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from modules import shared, devices, modelloader
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from modules.paths import models_path
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model_dir = "GFPGAN"
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user_path = None
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model_path = os.path.join(models_path, model_dir)
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model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
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have_gfpgan = False
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loaded_gfpgan_model = None
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def gfpgann():
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global loaded_gfpgan_model
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global model_path
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if loaded_gfpgan_model is not None:
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loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
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return loaded_gfpgan_model
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if gfpgan_constructor is None:
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return None
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models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
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if len(models) == 1 and "http" in models[0]:
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model_file = models[0]
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elif len(models) != 0:
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latest_file = max(models, key=os.path.getctime)
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model_file = latest_file
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else:
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print("Unable to load gfpgan model!")
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return None
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if hasattr(facexlib.detection.retinaface, 'device'):
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facexlib.detection.retinaface.device = devices.device_gfpgan
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model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
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loaded_gfpgan_model = model
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return model
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def send_model_to(model, device):
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model.gfpgan.to(device)
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model.face_helper.face_det.to(device)
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model.face_helper.face_parse.to(device)
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def gfpgan_fix_faces(np_image):
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model = gfpgann()
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if model is None:
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return np_image
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send_model_to(model, devices.device_gfpgan)
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np_image_bgr = np_image[:, :, ::-1]
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cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
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np_image = gfpgan_output_bgr[:, :, ::-1]
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model.face_helper.clean_all()
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if shared.opts.face_restoration_unload:
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send_model_to(model, devices.cpu)
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return np_image
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gfpgan_constructor = None
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def setup_model(dirname):
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global model_path
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if not os.path.exists(model_path):
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os.makedirs(model_path)
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try:
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from gfpgan import GFPGANer
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from facexlib import detection, parsing
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global user_path
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global have_gfpgan
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global gfpgan_constructor
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load_file_from_url_orig = gfpgan.utils.load_file_from_url
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facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
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facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
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def my_load_file_from_url(**kwargs):
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return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
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def facex_load_file_from_url(**kwargs):
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return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
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def facex_load_file_from_url2(**kwargs):
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return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
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gfpgan.utils.load_file_from_url = my_load_file_from_url
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facexlib.detection.load_file_from_url = facex_load_file_from_url
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facexlib.parsing.load_file_from_url = facex_load_file_from_url2
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user_path = dirname
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have_gfpgan = True
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gfpgan_constructor = GFPGANer
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class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
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def name(self):
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return "GFPGAN"
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def restore(self, np_image):
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return gfpgan_fix_faces(np_image)
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shared.face_restorers.append(FaceRestorerGFPGAN())
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except Exception:
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print("Error setting up GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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