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import os |
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from PIL import Image |
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import torch |
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import gradio as gr |
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import torch |
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torch.backends.cudnn.benchmark = True |
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from torchvision import transforms, utils |
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from util import * |
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from PIL import Image |
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import math |
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import random |
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import numpy as np |
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from torch import nn, autograd, optim |
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from torch.nn import functional as F |
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from tqdm import tqdm |
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import lpips |
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from model import * |
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from copy import deepcopy |
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import imageio |
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import os |
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import sys |
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import numpy as np |
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from PIL import Image |
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import torch |
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import torchvision.transforms as transforms |
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from argparse import Namespace |
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from e4e.models.psp import pSp |
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from util import * |
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from huggingface_hub import hf_hub_download |
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device= 'cpu' |
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model_path_e = hf_hub_download(repo_id="akhaliq/JoJoGAN_e4e_ffhq_encode", filename="e4e_ffhq_encode.pt") |
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ckpt = torch.load(model_path_e, map_location='cpu') |
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opts = ckpt['opts'] |
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opts['checkpoint_path'] = model_path_e |
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opts= Namespace(**opts) |
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net = pSp(opts, device).eval().to(device) |
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@ torch.no_grad() |
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def projection(img, name, device='cuda'): |
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transform = transforms.Compose( |
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[ |
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transforms.Resize(256), |
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transforms.CenterCrop(256), |
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transforms.ToTensor(), |
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transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), |
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] |
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) |
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img = transform(img).unsqueeze(0).to(device) |
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images, w_plus = net(img, randomize_noise=False, return_latents=True) |
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result_file = {} |
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result_file['latent'] = w_plus[0] |
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torch.save(result_file, name) |
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return w_plus[0] |
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device = 'cpu' |
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latent_dim = 512 |
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model_path_s = hf_hub_download(repo_id="akhaliq/jojogan-stylegan2-ffhq-config-f", filename="stylegan2-ffhq-config-f.pt") |
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original_generator = Generator(1024, latent_dim, 8, 2).to(device) |
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ckpt = torch.load(model_path_s, map_location=lambda storage, loc: storage) |
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original_generator.load_state_dict(ckpt["g_ema"], strict=False) |
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mean_latent = original_generator.mean_latent(10000) |
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generatorjojo = deepcopy(original_generator) |
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generatordisney = deepcopy(original_generator) |
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generatorjinx = deepcopy(original_generator) |
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generatorcaitlyn = deepcopy(original_generator) |
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generatoryasuho = deepcopy(original_generator) |
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generatorarcanemulti = deepcopy(original_generator) |
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generatorart = deepcopy(original_generator) |
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generatorspider = deepcopy(original_generator) |
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generatorsketch = deepcopy(original_generator) |
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transform = transforms.Compose( |
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[ |
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transforms.Resize((1024, 1024)), |
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transforms.ToTensor(), |
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), |
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] |
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) |
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modeljojo = hf_hub_download(repo_id="akhaliq/JoJoGAN-jojo", filename="jojo_preserve_color.pt") |
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ckptjojo = torch.load(modeljojo, map_location=lambda storage, loc: storage) |
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generatorjojo.load_state_dict(ckptjojo["g"], strict=False) |
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modeldisney = hf_hub_download(repo_id="akhaliq/jojogan-disney", filename="disney_preserve_color.pt") |
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ckptdisney = torch.load(modeldisney, map_location=lambda storage, loc: storage) |
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generatordisney.load_state_dict(ckptdisney["g"], strict=False) |
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modeljinx = hf_hub_download(repo_id="akhaliq/jojo-gan-jinx", filename="arcane_jinx_preserve_color.pt") |
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ckptjinx = torch.load(modeljinx, map_location=lambda storage, loc: storage) |
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generatorjinx.load_state_dict(ckptjinx["g"], strict=False) |
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modelcaitlyn = hf_hub_download(repo_id="akhaliq/jojogan-arcane", filename="arcane_caitlyn_preserve_color.pt") |
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ckptcaitlyn = torch.load(modelcaitlyn, map_location=lambda storage, loc: storage) |
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generatorcaitlyn.load_state_dict(ckptcaitlyn["g"], strict=False) |
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modelyasuho = hf_hub_download(repo_id="akhaliq/JoJoGAN-jojo", filename="jojo_yasuho_preserve_color.pt") |
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ckptyasuho = torch.load(modelyasuho, map_location=lambda storage, loc: storage) |
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generatoryasuho.load_state_dict(ckptyasuho["g"], strict=False) |
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model_arcane_multi = hf_hub_download(repo_id="akhaliq/jojogan-arcane", filename="arcane_multi_preserve_color.pt") |
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ckptarcanemulti = torch.load(model_arcane_multi, map_location=lambda storage, loc: storage) |
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generatorarcanemulti.load_state_dict(ckptarcanemulti["g"], strict=False) |
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modelart = hf_hub_download(repo_id="akhaliq/jojo-gan-art", filename="art.pt") |
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ckptart = torch.load(modelart, map_location=lambda storage, loc: storage) |
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generatorart.load_state_dict(ckptart["g"], strict=False) |
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modelSpiderverse = hf_hub_download(repo_id="akhaliq/jojo-gan-spiderverse", filename="Spiderverse-face-500iters-8face.pt") |
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ckptspider = torch.load(modelSpiderverse, map_location=lambda storage, loc: storage) |
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generatorspider.load_state_dict(ckptspider["g"], strict=False) |
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modelSketch = hf_hub_download(repo_id="akhaliq/jojogan-sketch", filename="sketch_multi.pt") |
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ckptsketch = torch.load(modelSketch, map_location=lambda storage, loc: storage) |
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generatorsketch.load_state_dict(ckptsketch["g"], strict=False) |
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def inference(img, model): |
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img.save('out.jpg') |
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aligned_face = align_face('out.jpg') |
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my_w = projection(aligned_face, "test.pt", device).unsqueeze(0) |
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if model == 'JoJo': |
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with torch.no_grad(): |
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my_sample = generatorjojo(my_w, input_is_latent=True) |
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elif model == 'Disney': |
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with torch.no_grad(): |
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my_sample = generatordisney(my_w, input_is_latent=True) |
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elif model == 'Jinx': |
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with torch.no_grad(): |
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my_sample = generatorjinx(my_w, input_is_latent=True) |
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elif model == 'Caitlyn': |
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with torch.no_grad(): |
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my_sample = generatorcaitlyn(my_w, input_is_latent=True) |
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elif model == 'Yasuho': |
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with torch.no_grad(): |
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my_sample = generatoryasuho(my_w, input_is_latent=True) |
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elif model == 'Arcane Multi': |
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with torch.no_grad(): |
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my_sample = generatorarcanemulti(my_w, input_is_latent=True) |
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elif model == 'Art': |
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with torch.no_grad(): |
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my_sample = generatorart(my_w, input_is_latent=True) |
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elif model == 'Spider-Verse': |
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with torch.no_grad(): |
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my_sample = generatorspider(my_w, input_is_latent=True) |
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else: |
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with torch.no_grad(): |
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my_sample = generatorsketch(my_w, input_is_latent=True) |
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npimage = my_sample[0].permute(1, 2, 0).detach().numpy() |
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imageio.imwrite('filename.jpeg', npimage) |
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return 'filename.jpeg' |
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title = "JoJoGAN" |
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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." |
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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>" |
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examples=[['mona.png','Jinx']] |
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gr.Interface(inference, [gr.inputs.Image(type="pil"),gr.inputs.Dropdown(choices=['JoJo', 'Disney','Jinx','Caitlyn','Yasuho','Arcane Multi','Art','Spider-Verse','Sketch'], type="value", default='JoJo', label="Model")], gr.outputs.Image(type="file"),title=title,description=description,article=article,allow_flagging=False,examples=examples,allow_screenshot=False).launch() |
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