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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 = '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 = 'ffhq.pkl' |
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paths_config.checkpoints_dir = 'checkpoints' |
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paths_config.style_clip_pretrained_mappers = '' |
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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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def load_generator(model_id): |
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with open(f'{paths_config.checkpoints_dir}/model_{model_id}_file.pt', 'rb') as f_new: |
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new_G = torch.load(f_new).cuda() |
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return new_G |
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def tensor_to_pil(img): |
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img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()[0] |
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plt.axis('off') |
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resized_image = Image.fromarray(img,mode='RGB').resize((256,256)) |
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return resized_image |
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def tune(): |
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model_id = run_PTI(run_name='',use_wandb=False, use_multi_id_training=False) |
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w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}' |
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embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/file' |
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w_pivot = torch.load(f'{embedding_dir}/0.pt') |
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new_G = load_generator(model_id) |
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new_image = new_G.synthesis(w_pivot, noise_mode='const', force_fp32 = True) |
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tensor_to_pil(new_image).save("output/out.png") |
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if __name__ == '__main__': |
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tune() |
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