gfpgan: just download the damn model
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@ -1,6 +1,7 @@
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import os
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import sys
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import traceback
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from glob import glob
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from modules import shared, devices
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from modules.shared import cmd_opts
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@ -11,14 +12,20 @@ import modules.face_restoration
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def gfpgan_model_path():
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from modules.shared import cmd_opts
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filemask = 'GFPGAN*.pth'
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if cmd_opts.gfpgan_model is not None:
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return cmd_opts.gfpgan_model
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places = [script_path, '.', os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models')]
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files = [cmd_opts.gfpgan_model] + [os.path.join(dirname, cmd_opts.gfpgan_model) for dirname in places]
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found = [x for x in files if os.path.exists(x)]
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if len(found) == 0:
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raise Exception("GFPGAN model not found in paths: " + ", ".join(files))
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filename = None
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for place in places:
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filename = next(iter(glob(os.path.join(place, filemask))), None)
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if filename is not None:
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break
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return found[0]
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return filename
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loaded_gfpgan_model = None
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@ -34,7 +41,7 @@ def gfpgan():
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if gfpgan_constructor is None:
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return None
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model = gfpgan_constructor(model_path=gfpgan_model_path(), upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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model = gfpgan_constructor(model_path=gfpgan_model_path() or 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth', upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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model.gfpgan.to(shared.device)
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loaded_gfpgan_model = model
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@ -2,7 +2,6 @@ import sys
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import argparse
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import json
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import os
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from glob import glob
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import gradio as gr
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import tqdm
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@ -22,7 +21,7 @@ parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs
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parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
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parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), help="path to directory with stable diffusion checkpoints",)
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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
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parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=next(iter(glob('GFPGAN*.pth')), ''))
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parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
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parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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