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| import os | |
| from PIL import Image | |
| import torch | |
| import gradio as gr | |
| 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-AG7JPTWc9REBrkll3OyEpZwSOWhlX0j") | |
| 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 | |
| generator = 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=1-7UlCppmiG4DKbhYDNbIZTc6mHy9JMWJ") | |
| os.system("cp e4e_ffhq_encode.pt models/e4e_ffhq_encode.pt") | |
| plt.rcParams['figure.dpi'] = 150 | |
| os.system("gdown https://drive.google.com/uc?id=1-8E0PFT37v5fZs-61oIrFbNpE28Unp2y") | |
| def inference(img): | |
| my_w = e4e_projection(img, "test.pt", device).unsqueeze(0) | |
| plt.rcParams['figure.dpi'] = 150 | |
| pretrained = 'jojo' #@param ['supergirl', 'arcane_jinx', 'arcane_caitlyn', 'jojo_yasuho', 'jojo', 'disney'] | |
| #@markdown Preserve color tries to preserve color of original image by limiting family of allowable transformations. Otherwise, the stylized image will inherit the colors of the reference images, leading to heavier stylizations. | |
| preserve_color = False | |
| ckpt = torch.load('jojo.pt', map_location=lambda storage, loc: storage) | |
| generator.load_state_dict(ckpt["g"], strict=False) | |
| with torch.no_grad(): | |
| generator.eval() | |
| original_my_sample = original_generator(my_w, input_is_latent=True) | |
| my_sample = generator(my_w, input_is_latent=True) | |
| npimage = my_sample[0].permute(1, 2, 0).detach().numpy()[:,:,::-1] | |
| 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://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']] | |
| gr.Interface(inference, [gr.inputs.Image(type="pil")], gr.outputs.Image(type="file"),title=title,description=description,article=article,enable_queue=True,allow_flagging=False,examples=examples).launch() | |