36 lines
2.4 KiB
Python
36 lines
2.4 KiB
Python
import torchvision
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import argparse
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from argparse import Namespace
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from optimization.run_optimization import main
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parser = argparse.ArgumentParser()
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parser.add_argument("--description", type=str, default="a person with purple hair",
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help="the text that guides the editing/generation")
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parser.add_argument("--ckpt", type=str, default="./pretrained_models/stylegan2-ffhq-config-f.pt",
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help="pretrained StyleGAN2 weights")
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parser.add_argument("--stylegan_size", type=int, default=1024, help="StyleGAN resolution")
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parser.add_argument("--lr_rampup", type=float, default=0.05)
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parser.add_argument("--lr", type=float, default=0.1)
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parser.add_argument("--step", type=int, default=300, help="number of optimization steps")
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parser.add_argument("--mode", type=str, default="edit", choices=["edit", "free_generation"],
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help="choose between edit an image an generate a free one")
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parser.add_argument("--l2_lambda", type=float, default=0.008,
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help="weight of the latent distance (used for editing only)")
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parser.add_argument("--latent_path", type=str, default="/home/ly/StyleCLIP-main/pretrained_models/latent_code/style3.pt",
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help="starts the optimization from the given latent code if provided. Otherwise, starts from"
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"the mean latent in a free generation, and from a random one in editing. "
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"Expects a .pt format")
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parser.add_argument("--truncation", type=float, default=0.7,
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help="used only for the initial latent vector, and only when a latent code path is"
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"not provided")
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parser.add_argument("--save_intermediate_image_every", type=int, default=20,
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help="if > 0 then saves intermidate results during the optimization")
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parser.add_argument("--results_dir", type=str, default="results")
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parser.add_argument('--work_in_stylespace', default=False, action='store_true', help="trains a mapper in S instead of W+")
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parser.add_argument('--ir_se50_weights', default='pretrained_models/model_ir_se50.pth', type=str, help="Path to facial recognition network used in ID loss")
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parser.add_argument('--id_lambda', default=0.1, type=float, help='ID loss multiplier factor')
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args = vars(parser.parse_args())
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result_image = main(Namespace(**args))
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torchvision.utils.save_image(result_image.detach().cpu(), f"results/final_result.png", normalize=True, scale_each=True,
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range=(-1, 1)) |