Spaces:
Runtime error
Runtime error
| import os | |
| from PIL import Image | |
| import torch | |
| import gradio as gr | |
| os.system("git clone https://github.com/mchong6/JoJoGAN.git") | |
| os.chdir("JoJoGAN") | |
| 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 google.colab import files | |
| from copy import deepcopy | |
| from pydrive.auth import GoogleAuth | |
| from pydrive.drive import GoogleDrive | |
| from google.colab import auth | |
| from oauth2client.client import GoogleCredentials | |
| 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' | |
| download_with_pydrive = True #@param {type:"boolean"} | |
| drive_ids = { | |
| "stylegan2-ffhq-config-f.pt": "1Yr7KuD959btpmcKGAUsbAk5rPjX2MytK", | |
| "e4e_ffhq_encode.pt": "1o6ijA3PkcewZvwJJ73dJ0fxhndn0nnh7", | |
| "restyle_psp_ffhq_encode.pt": "1nbxCIVw9H3YnQsoIPykNEFwWJnHVHlVd", | |
| "arcane_caitlyn.pt": "1gOsDTiTPcENiFOrhmkkxJcTURykW1dRc", | |
| "arcane_caitlyn_preserve_color.pt": "1cUTyjU-q98P75a8THCaO545RTwpVV-aH", | |
| "arcane_jinx_preserve_color.pt": "1jElwHxaYPod5Itdy18izJk49K1nl4ney", | |
| "arcane_jinx.pt": "1quQ8vPjYpUiXM4k1_KIwP4EccOefPpG_", | |
| "disney.pt": "1zbE2upakFUAx8ximYnLofFwfT8MilqJA", | |
| "disney_preserve_color.pt": "1Bnh02DjfvN_Wm8c4JdOiNV4q9J7Z_tsi", | |
| "jojo.pt": "13cR2xjIBj8Ga5jMO7gtxzIJj2PDsBYK4", | |
| "jojo_preserve_color.pt": "1ZRwYLRytCEKi__eT2Zxv1IlV6BGVQ_K2", | |
| "jojo_yasuho.pt": "1grZT3Gz1DLzFoJchAmoj3LoM9ew9ROX_", | |
| "jojo_yasuho_preserve_color.pt": "1SKBu1h0iRNyeKBnya_3BBmLr4pkPeg_L", | |
| "supergirl.pt": "1L0y9IYgzLNzB-33xTpXpecsKU-t9DpVC", | |
| "supergirl_preserve_color.pt": "1VmKGuvThWHym7YuayXxjv0fSn32lfDpE", | |
| } | |
| os.system("gdown https://drive.google.com/uc?id=1Yr7KuD959btpmcKGAUsbAk5rPjX2MytK") | |
| os.system("mv stylegan2-ffhq-config-f.pt models/stylegan2-ffhq-config-f.pt") | |
| latent_dim = 512 | |
| # Load original generator | |
| original_generator = Generator(1024, latent_dim, 8, 2).to(device) | |
| ckpt = torch.load(os.path.join('models', ckpt), 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)), | |
| ] | |
| ) | |
| plt.rcParams['figure.dpi'] = 150 | |
| filepath = f'test_input/{filename}' | |
| name = strip_path_extension(filepath)+'.pt' | |
| aligned_face = align_face(filepath) | |
| os.system("gdown https://drive.google.com/uc?id=1o6ijA3PkcewZvwJJ73dJ0fxhndn0nnh7") | |
| os.system("mv e4e_ffhq_encode.pt models/e4e_ffhq_encode.pt") | |
| my_w = e4e_projection(aligned_face, name, 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 #@param{type:"boolean"} | |
| if preserve_color: | |
| ckpt = f'{pretrained}_preserve_color.pt' | |
| else: | |
| ckpt = f'{pretrained}.pt' | |
| downloader.download_file(ckpt) | |
| ckpt = torch.load(os.path.join('models', ckpt), map_location=lambda storage, loc: storage) | |
| generator.load_state_dict(ckpt["g"], strict=False) | |
| #@title Generate results | |
| n_sample = 1#@param {type:"number"} | |
| seed = 3000 #@param {type:"number"} | |
| torch.manual_seed(seed) | |
| with torch.no_grad(): | |
| generator.eval() | |
| z = torch.randn(n_sample, latent_dim, device=device) | |
| original_sample = original_generator([z], truncation=0.7, truncation_latent=mean_latent) | |
| sample = generator([z], truncation=0.7, truncation_latent=mean_latent) | |
| original_my_sample = original_generator(my_w, input_is_latent=True) | |
| my_sample = generator(my_w, input_is_latent=True) | |
| # display reference images | |
| style_path = f'style_images_aligned/{pretrained}.png' | |
| style_image = transform(Image.open(style_path)).unsqueeze(0).to(device) | |
| face = transform(aligned_face).unsqueeze(0).to(device) | |
| my_output = torch.cat([style_image, face, my_sample], 0) | |
| display_image(utils.make_grid(my_output, normalize=True, range=(-1, 1)), title='My sample') | |
| output = torch.cat([original_sample, sample], 0) | |
| display_image(utils.make_grid(output, normalize=True, range=(-1, 1), nrow=n_sample), title='Random samples') | |
| def inference(img, ver): | |
| if ver == 'version 2 (🔺 robustness,🔻 stylization)': | |
| out = face2paint(model2, img) | |
| else: | |
| out = face2paint(model1, img) | |
| return out | |
| title = "AnimeGANv2" | |
| description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below." | |
| article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center> <p style='text-align: center'>samples from repo: <img src='https://user-images.githubusercontent.com/26464535/129888683-98bb6283-7bb8-4d1a-a04a-e795f5858dcf.gif' alt='animation'/> <img src='https://user-images.githubusercontent.com/26464535/137619176-59620b59-4e20-4d98-9559-a424f86b7f24.jpg' alt='animation'/><img src='https://user-images.githubusercontent.com/26464535/127134790-93595da2-4f8b-4aca-a9d7-98699c5e6914.jpg' alt='animation'/></p>" | |
| examples=[['groot.jpeg','version 2 (🔺 robustness,🔻 stylization)'],['bill.png','version 1 (🔺 stylization, 🔻 robustness)'],['tony.png','version 1 (🔺 stylization, 🔻 robustness)'],['elon.png','version 2 (🔺 robustness,🔻 stylization)'],['IU.png','version 1 (🔺 stylization, 🔻 robustness)'],['billie.png','version 2 (🔺 robustness,🔻 stylization)'],['will.png','version 2 (🔺 robustness,🔻 stylization)'],['beyonce.png','version 1 (🔺 stylization, 🔻 robustness)'],['gongyoo.jpeg','version 1 (🔺 stylization, 🔻 robustness)']] | |
| gr.Interface(inference, [gr.inputs.Image(type="pil"),gr.inputs.Radio(['version 1 (🔺 stylization, 🔻 robustness)','version 2 (🔺 robustness,🔻 stylization)'], type="value", default='version 2 (🔺 robustness,🔻 stylization)', label='version') | |
| ], gr.outputs.Image(type="pil"),title=title,description=description,article=article,enable_queue=True,examples=examples,allow_flagging=False).launch() | |