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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import argparse | |
| import functools | |
| import os | |
| import pickle | |
| import sys | |
| import subprocess | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| sys.path.append('.') | |
| sys.path.append('./Time_TravelRephotography') | |
| from utils import torch_helpers as th | |
| from argparse import Namespace | |
| from projector import ( | |
| ProjectorArguments, | |
| main, | |
| create_generator, | |
| make_image, | |
| ) | |
| input_path = '' | |
| spectral_sensitivity = 'b' | |
| TITLE = 'Time-TravelRephotography' | |
| DESCRIPTION = '''This is an unofficial demo for https://github.com/Time-Travel-Rephotography. | |
| ''' | |
| ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=Time-TravelRephotography" alt="visitor badge"/></center>' | |
| def image_create(seed: int, truncation_psi: float): | |
| args = ProjectorArguments().parse( | |
| args=[str(input_path)], | |
| namespace=Namespace( | |
| encoder_ckpt=f"checkpoint/encoder/checkpoint_{spectral_sensitivity}.pt", | |
| #gaussian=gaussian_radius, | |
| log_visual_freq=1000 | |
| )) | |
| device = th.device() | |
| generator = create_generator("stylegan2-ffhq-config-f.pt","feng2022/Time-TravelRephotography_stylegan2-ffhq-config-f",args,device) | |
| #generator = create_generator("checkpoint_b.pt.pth","feng2022/Time_TravelRephotography_checkpoint_b",args,device) | |
| latent = torch.randn((1, 512), device=device) | |
| img_out, _, _ = generator([latent]) | |
| imgs_arr = make_image(img_out) | |
| return imgs_arr[0]/255 | |
| def main(): | |
| torch.cuda.init() | |
| if torch.cuda.is_initialized(): | |
| ini = "True1" | |
| else: | |
| ini = "False1" | |
| result = subprocess.check_output(['nvidia-smi']) | |
| device = th.device() | |
| iface = gr.Interface( | |
| image_create, | |
| [ | |
| gr.inputs.Number(default=0, label='Seed'), | |
| gr.inputs.Slider( | |
| 0, 2, step=0.05, default=0.7, label='Truncation psi'), | |
| ], | |
| gr.outputs.Image(type='numpy', label='Output'), | |
| title=TITLE, | |
| description=DESCRIPTION, | |
| article=ARTICLE, | |
| ) | |
| iface.launch() | |
| if __name__ == '__main__': | |
| main() | |