File size: 4,744 Bytes
856b6b5 d3346dc 856b6b5 d3346dc 856b6b5 ffaad00 856b6b5 d3346dc 856b6b5 d3346dc 856b6b5 d3346dc 856b6b5 d3346dc 856b6b5 f7749e3 9dd4902 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
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 *
import urllib.request
#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="aijack/e4e", filename="e4e.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)
# Fetch image for analysis
img_url = "http://claireye.com.tw/img/230212a.jpg"
urllib.request.urlretrieve(img_url, "pose.jpg")
@ 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="aijack/stylegan2", filename="stylegan2.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)
generatorcaitlyn = deepcopy(original_generator)
generatorart = deepcopy(original_generator)
generatorsketch = 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="aijack/jojo", filename="jojo.pt")
ckptjojo = torch.load(modeljojo, map_location=lambda storage, loc: storage)
generatorjojo.load_state_dict(ckptjojo["g"], strict=False)
modelcaitlyn = hf_hub_download(repo_id="aijack/arcane", filename="arcane.pt")
ckptcaitlyn = torch.load(modelcaitlyn, map_location=lambda storage, loc: storage)
generatorcaitlyn.load_state_dict(ckptcaitlyn["g"], strict=False)
modelart = hf_hub_download(repo_id="aijack/art", filename="art.pt")
ckptart = torch.load(modelart, map_location=lambda storage, loc: storage)
generatorart.load_state_dict(ckptart["g"], strict=False)
modelSketch = hf_hub_download(repo_id="aijack/sketch", filename="sketch.pt")
ckptsketch = torch.load(modelSketch, map_location=lambda storage, loc: storage)
generatorsketch.load_state_dict(ckptsketch["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 == 'Caitlyn':
with torch.no_grad():
my_sample = generatorcaitlyn(my_w, input_is_latent=True)
elif model == 'Art':
with torch.no_grad():
my_sample = generatorart(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"
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='http://claireye.com.tw'>Claireye</a> | 2023</p>"
examples=[['pose.jpg','Art']]
gr.Interface(inference, [gr.inputs.Image(type="pil"),gr.inputs.Dropdown(choices=['JoJo', 'Caitlyn','Art','Sketch'], type="value", default='JoJo', label="Model")], gr.outputs.Image(type="numpy"),title=title,description=description,article=article,allow_flagging=False,examples=examples,allow_screenshot=False).launch()
|