| import click |
| import os |
| import sys |
| import pickle |
| import numpy as np |
| from PIL import Image |
| import torch |
| from configs import paths_config, hyperparameters, global_config |
| from IPython.display import display |
| import matplotlib.pyplot as plt |
| from scripts.latent_editor_wrapper import LatentEditorWrapper |
|
|
| image_dir_name = 'images' |
| use_multi_id_training = False |
| global_config.device = 'cuda' |
| paths_config.e4e = 'e4e_ffhq_encode.pt' |
| paths_config.input_data_id = image_dir_name |
| paths_config.input_data_path = f'{image_dir_name}' |
| paths_config.stylegan2_ada_ffhq = 'ffhq.pkl' |
| paths_config.checkpoints_dir = 'checkpoints' |
| paths_config.style_clip_pretrained_mappers = '' |
| hyperparameters.use_locality_regularization = False |
| hyperparameters.lpips_type = 'squeeze' |
|
|
| from scripts.run_pti import run_PTI |
|
|
| def load_generator(model_id): |
| with open(f'{paths_config.checkpoints_dir}/model_{model_id}_file.pt', 'rb') as f_new: |
| new_G = torch.load(f_new).cuda() |
| return new_G |
| |
| def tensor_to_pil(img): |
| img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()[0] |
| plt.axis('off') |
| resized_image = Image.fromarray(img,mode='RGB').resize((256,256)) |
| return resized_image |
|
|
| def tune(): |
| model_id = run_PTI(run_name='',use_wandb=False, use_multi_id_training=False) |
| w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}' |
| embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/file' |
| w_pivot = torch.load(f'{embedding_dir}/0.pt') |
| new_G = load_generator(model_id) |
| new_image = new_G.synthesis(w_pivot, noise_mode='const', force_fp32 = True) |
| tensor_to_pil(new_image).save("output/out.png") |
| |
| |
| if __name__ == '__main__': |
| tune() |
|
|
| |
|
|
