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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 = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(device)
data = data.to(device)
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()
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