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import click |
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import os |
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import sys |
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import pickle |
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import numpy as np |
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from PIL import Image |
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import torch |
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from configs import paths_config, hyperparameters, global_config |
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from IPython.display import display |
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import matplotlib.pyplot as plt |
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from scripts.latent_editor_wrapper import LatentEditorWrapper |
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image_dir_name = 'images' |
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use_multi_id_training = False |
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global_config.device = 'cuda' |
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paths_config.e4e = '/home/sayantan/PTI/pretrained_models/e4e_ffhq_encode.pt' |
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paths_config.input_data_id = image_dir_name |
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paths_config.input_data_path = f'{image_dir_name}' |
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paths_config.stylegan2_ada_ffhq = '/home/sayantan/PTI/pretrained_models/ffhq.pkl' |
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paths_config.checkpoints_dir = '/home/sayantan/PTI/' |
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paths_config.style_clip_pretrained_mappers = '/home/sayantan/PTI/pretrained_models' |
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hyperparameters.use_locality_regularization = False |
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hyperparameters.lpips_type = 'squeeze' |
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from scripts.run_pti import run_PTI |
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@click.command() |
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@click.pass_context |
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@click.option('--rname', prompt='wandb RUN NAME', help='The name to give for the wandb run') |
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def tune(ctx: click.Context,rname): |
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runn = wandb.init(project='PTI', entity='masc', name = rname) |
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model_id = run_PTI(run_name='',use_wandb=True, use_multi_id_training=False) |
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if __name__ == '__main__': |
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tune() |
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