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 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() convert = (npimage * 255).astype('uint8') return convert 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 = "

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samples from repo: animation

" examples=[['iu.jpeg']] gr.Interface(inference, [gr.inputs.Image(type="pil")], gr.outputs.Image(type="numpy"),title=title,description=description,article=article,enable_queue=True,allow_flagging=False,examples=examples).launch()