#!/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 from transformers import pipeline 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 = '
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' 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) 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()