85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
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import os
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import torch
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from facexlib.utils.face_restoration_helper import FaceRestoreHelper
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from gfpgan import GFPGANv1Clean, GFPGANer
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from torch.hub import get_dir
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class MyGFPGANer(GFPGANer):
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"""Helper for restoration with GFPGAN.
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It will detect and crop faces, and then resize the faces to 512x512.
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GFPGAN is used to restored the resized faces.
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The background is upsampled with the bg_upsampler.
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Finally, the faces will be pasted back to the upsample background image.
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Args:
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model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically).
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upscale (float): The upscale of the final output. Default: 2.
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arch (str): The GFPGAN architecture. Option: clean | original. Default: clean.
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channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
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bg_upsampler (nn.Module): The upsampler for the background. Default: None.
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"""
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def __init__(
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self,
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model_path,
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upscale=2,
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arch="clean",
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channel_multiplier=2,
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bg_upsampler=None,
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device=None,
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):
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self.upscale = upscale
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self.bg_upsampler = bg_upsampler
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# initialize model
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self.device = (
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torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if device is None
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else device
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)
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# initialize the GFP-GAN
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if arch == "clean":
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self.gfpgan = GFPGANv1Clean(
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out_size=512,
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num_style_feat=512,
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channel_multiplier=channel_multiplier,
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decoder_load_path=None,
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fix_decoder=False,
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num_mlp=8,
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input_is_latent=True,
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different_w=True,
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narrow=1,
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sft_half=True,
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)
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elif arch == "RestoreFormer":
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from gfpgan.archs.restoreformer_arch import RestoreFormer
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self.gfpgan = RestoreFormer()
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hub_dir = get_dir()
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model_dir = os.path.join(hub_dir, "checkpoints")
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# initialize face helper
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self.face_helper = FaceRestoreHelper(
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upscale,
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face_size=512,
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crop_ratio=(1, 1),
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det_model="retinaface_resnet50",
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save_ext="png",
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use_parse=True,
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device=self.device,
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model_rootpath=model_dir,
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)
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loadnet = torch.load(model_path)
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if "params_ema" in loadnet:
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keyname = "params_ema"
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else:
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keyname = "params"
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self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
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self.gfpgan.eval()
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self.gfpgan = self.gfpgan.to(self.device)
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