app.py

import os
from PIL import Image
import torch
import gradio as gr

os.system("pip install dlib")
import torch
torch.backends.cudnn.benchmark = True
from torchvision import transforms, utils
from util import *
from PIL import Image
import math
import random

import numpy as np
from torch import nn, autograd, optim
from torch.nn import functional as F
from tqdm import tqdm
import lpips
from model import *
from e4e_projection import projection as e4e_projection

from copy import deepcopy
import imageio

os.makedirs('inversion_codes', exist_ok=True)
os.makedirs('style_images', exist_ok=True)
os.makedirs('style_images_aligned', exist_ok=True)
os.makedirs('models', exist_ok=True)

os.system("wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
os.system("bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2")
os.system("mv shape_predictor_68_face_landmarks.dat models/dlibshape_predictor_68_face_landmarks.dat")


device = 'cpu' 

os.system("gdown https://drive.google.com/uc?id=1_cTsjqzD_X9DK3t3IZE53huKgnzj_btZ")

latent_dim = 512

# Load original generator
original_generator = Generator(1024, latent_dim, 8, 2).to(device)
ckpt = torch.load('stylegan2-ffhq-config-f.pt', map_location=lambda storage, loc: storage)
original_generator.load_state_dict(ckpt["g_ema"], strict=False)
mean_latent = original_generator.mean_latent(10000)

# to be finetuned generator
generatorjojo = deepcopy(original_generator)

generatordisney = deepcopy(original_generator)

generatorjinx = deepcopy(original_generator)



transform = transforms.Compose(
    [
        transforms.Resize((1024, 1024)),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
    ]
)

os.system("gdown https://drive.google.com/uc?id=1jtCg8HQ6RlTmLdnbT2PfW1FJ2AYkWqsK")
os.system("cp e4e_ffhq_encode.pt models/e4e_ffhq_encode.pt")

os.system("gdown https://drive.google.com/uc?id=1-8E0PFT37v5fZs-61oIrFbNpE28Unp2y")

ckptjojo = torch.load('jojo.pt', map_location=lambda storage, loc: storage)
generatorjojo.load_state_dict(ckptjojo["g"], strict=False)

os.system("gdown https://drive.google.com/uc?id=1Bnh02DjfvN_Wm8c4JdOiNV4q9J7Z_tsi")

ckptdisney = torch.load('disney_preserve_color.pt', map_location=lambda storage, loc: storage)
generatordisney.load_state_dict(ckptdisney["g"], strict=False)

os.system("gdown https://drive.google.com/uc?id=1jElwHxaYPod5Itdy18izJk49K1nl4ney")

ckptjinx = torch.load('arcane_jinx_preserve_color.pt', map_location=lambda storage, loc: storage)
generatorjinx.load_state_dict(ckptjinx["g"], strict=False)


def inference(img, model):    
    aligned_face = align_face(img)
    my_w = e4e_projection(aligned_face, "test.pt", device).unsqueeze(0)
    if model == 'JoJo':
        with torch.no_grad():
            my_sample = generatorjojo(my_w, input_is_latent=True)  
    elif model == 'Disney':
        with torch.no_grad():
            my_sample = generatordisney(my_w, input_is_latent=True)
    else:
        with torch.no_grad():
            my_sample = generatorjinx(my_w, input_is_latent=True)
            
    
    npimage = my_sample[0].permute(1, 2, 0).detach().numpy()
    imageio.imwrite('filename.jpeg', npimage)
    return 'filename.jpeg'
  
title = "JoJoGAN"
description = "Gradio Demo for JoJoGAN: One Shot Face Stylization. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."

article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.11641' target='_blank'>JoJoGAN: One Shot Face Stylization</a>| <a href='https://github.com/mchong6/JoJoGAN' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_jojogan' alt='visitor badge'></center> <p style='text-align: center'>samples from repo: <img src='https://raw.githubusercontent.com/mchong6/JoJoGAN/main/teaser.jpg' alt='animation'/></p>"

examples=[['iu.jpeg','Jinx']]
gr.Interface(inference, [gr.inputs.Image(type="filepath",shape=(256,256)),gr.inputs.Dropdown(choices=['JoJo', 'Disney','Jinx'], type="value", default='JoJo', label="Model")], gr.outputs.Image(type="file"),title=title,description=description,article=article,enable_queue=True,allow_flagging=False,examples=examples).launch()