import os os.system("pip install gradio==2.8.0b5") os.system("pip install -r requirements.txt") os.system("pip freeze") 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 import os import sys import numpy as np from PIL import Image import torch import torchvision.transforms as transforms from argparse import Namespace from e4e.models.psp import pSp from util import * from huggingface_hub import hf_hub_download device= 'cpu' model_path_e = hf_hub_download(repo_id="akhaliq/JoJoGAN_e4e_ffhq_encode", filename="e4e_ffhq_encode.pt") ckpt = torch.load(model_path_e, map_location='cpu') opts = ckpt['opts'] opts['checkpoint_path'] = model_path_e opts= Namespace(**opts) net = pSp(opts, device).eval().to(device) @ torch.no_grad() def projection(img, name, device='cuda'): transform = transforms.Compose( [ transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), ] ) img = transform(img).unsqueeze(0).to(device) images, w_plus = net(img, randomize_noise=False, return_latents=True) result_file = {} result_file['latent'] = w_plus[0] torch.save(result_file, name) return w_plus[0] device = 'cpu' latent_dim = 512 model_path_s = hf_hub_download(repo_id="akhaliq/jojogan-stylegan2-ffhq-config-f", filename="stylegan2-ffhq-config-f.pt") original_generator = Generator(1024, latent_dim, 8, 2).to(device) ckpt = torch.load(model_path_s, map_location=lambda storage, loc: storage) original_generator.load_state_dict(ckpt["g_ema"], strict=False) mean_latent = original_generator.mean_latent(10000) generatorjojo = deepcopy(original_generator) generatordisney = deepcopy(original_generator) generatorjinx = deepcopy(original_generator) generatorcaitlyn = deepcopy(original_generator) generatoryasuho = deepcopy(original_generator) generatorarcanemulti = deepcopy(original_generator) generatorart = deepcopy(original_generator) generatorspider = deepcopy(original_generator) generatorsketch = deepcopy(original_generator) generatordisneynoc = 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)), ] ) modeljojo = hf_hub_download(repo_id="akhaliq/JoJoGAN-jojo", filename="jojo_preserve_color.pt") ckptjojo = torch.load(modeljojo, map_location=lambda storage, loc: storage) generatorjojo.load_state_dict(ckptjojo["g"], strict=False) modeldisney = hf_hub_download(repo_id="akhaliq/jojogan-disney", filename="disney_preserve_color.pt") ckptdisney = torch.load(modeldisney, map_location=lambda storage, loc: storage) generatordisney.load_state_dict(ckptdisney["g"], strict=False) modeljinx = hf_hub_download(repo_id="akhaliq/jojo-gan-jinx", filename="arcane_jinx_preserve_color.pt") ckptjinx = torch.load(modeljinx, map_location=lambda storage, loc: storage) generatorjinx.load_state_dict(ckptjinx["g"], strict=False) modelcaitlyn = hf_hub_download(repo_id="akhaliq/jojogan-arcane", filename="arcane_caitlyn_preserve_color.pt") ckptcaitlyn = torch.load(modelcaitlyn, map_location=lambda storage, loc: storage) generatorcaitlyn.load_state_dict(ckptcaitlyn["g"], strict=False) modelyasuho = hf_hub_download(repo_id="akhaliq/JoJoGAN-jojo", filename="jojo_yasuho_preserve_color.pt") ckptyasuho = torch.load(modelyasuho, map_location=lambda storage, loc: storage) generatoryasuho.load_state_dict(ckptyasuho["g"], strict=False) model_arcane_multi = hf_hub_download(repo_id="akhaliq/jojogan-arcane", filename="arcane_multi_preserve_color.pt") ckptarcanemulti = torch.load(model_arcane_multi, map_location=lambda storage, loc: storage) generatorarcanemulti.load_state_dict(ckptarcanemulti["g"], strict=False) modelart = hf_hub_download(repo_id="akhaliq/jojo-gan-art", filename="art.pt") ckptart = torch.load(modelart, map_location=lambda storage, loc: storage) generatorart.load_state_dict(ckptart["g"], strict=False) modelSpiderverse = hf_hub_download(repo_id="akhaliq/jojo-gan-spiderverse", filename="Spiderverse-face-500iters-8face.pt") ckptspider = torch.load(modelSpiderverse, map_location=lambda storage, loc: storage) generatorspider.load_state_dict(ckptspider["g"], strict=False) modelSketch = hf_hub_download(repo_id="akhaliq/jojogan-sketch", filename="sketch_multi.pt") ckptsketch = torch.load(modelSketch, map_location=lambda storage, loc: storage) generatorsketch.load_state_dict(ckptsketch["g"], strict=False) modeldisneynoc = hf_hub_download(repo_id="algomuffin/disney", filename="disney.pt") ckptMy = torch.load(modeldisneynoc, map_location=lambda storage, loc: storage) generatordisneynoc.load_state_dict(ckptMy["g"], strict=False) def inference(img, model): img.save('out.jpg') aligned_face = align_face('out.jpg') my_w = 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 = generatordisneynoc(my_w, input_is_latent=True) elif model == 'Jinx': with torch.no_grad(): my_sample = generatorjinx(my_w, input_is_latent=True) elif model == 'Caitlyn': with torch.no_grad(): my_sample = generatorcaitlyn(my_w, input_is_latent=True) elif model == 'Yasuho': with torch.no_grad(): my_sample = generatoryasuho(my_w, input_is_latent=True) elif model == 'Arcane Multi': with torch.no_grad(): my_sample = generatorarcanemulti(my_w, input_is_latent=True) elif model == 'Art': with torch.no_grad(): my_sample = generatorart(my_w, input_is_latent=True) elif model == 'Spider-Verse': with torch.no_grad(): my_sample = generatorspider(my_w, input_is_latent=True) else: with torch.no_grad(): my_sample = generatorsketch(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_fork" description = "Gradio Demo for JoJoGAN: This is a fork made by Alessandro Miragliotta in order to test the network with the Disney no-preserve color model. To use it, simply upload your image, or click one of the examples to load them." article = "

JoJoGAN: One Shot Face Stylization| Github Repo Pytorch

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" examples=[['mona.png','Disney'],['man.jpg','Disney'],['iu.jpeg','Disney'],['baby-face.jpg','Disney']] gr.Interface(inference, [gr.inputs.Image(type="pil"),gr.inputs.Dropdown(choices=['Disney'], type="value", default='Disney', label="Model")], gr.outputs.Image(type="file"),title=title,description=description,article=article,allow_flagging=False,examples=examples,allow_screenshot=False).launch(enable_queue=True, cache_examples=True)