File size: 4,270 Bytes
1db31b3 169a997 212f725 169a997 979953f 1db31b3 212f725 fb7dab1 212f725 169a997 27a57d1 169a997 84e27df 1db31b3 979953f c03b20c 979953f 1db31b3 212f725 1db31b3 212f725 59506ce 979953f 1db31b3 271f7dc 1db31b3 1b3a9d6 200df21 |
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 |
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 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)
#MODELS
generatorzombie = deepcopy(original_generator)
generatorhulk = deepcopy(original_generator)
generatorjojo = deepcopy(original_generator)
generatorwalker = 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)),
]
)
#HULK
modelhulk = hf_hub_download(repo_id="Awesimo/jojogan-hulk", filename="hulk.pt")
ckpthulk = torch.load(modelhulk, map_location=lambda storage, loc: storage)
generatorhulk.load_state_dict(ckpthulk["g"], strict=False)
#ZOMBIE
modelzombie = hf_hub_download(repo_id="Awesimo/jojogan-zombie", filename="zombie.pt")
ckptzombie = torch.load(modelzombie, map_location=lambda storage, loc: storage)
generatorzombie.load_state_dict(ckptzombie["g"], strict=False)
#WHITE WALKER
modelwalker = hf_hub_download(repo_id="Awesimo/jojogan-white-walker", filename="white_walker_v2.pt")
ckptwalker = torch.load(modelwalker, map_location=lambda storage, loc: storage)
generatorwalker.load_state_dict(ckptwalker["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 == 'Hulk':
with torch.no_grad():
my_sample = generatorhulk(my_w, input_is_latent=True)
elif model == 'Zombie':
with torch.no_grad():
my_sample = generatorzombie(my_w, input_is_latent=True)
elif model == 'White-Walker':
with torch.no_grad():
my_sample = generatorwalker(my_w, input_is_latent=True)
else:
with torch.no_grad():
my_sample = generatorzombie(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 Test 🤖"
examples=[['assets/samples/image01.jpg','Hulk'],['assets/samples/image02.jpg','Zombie'],['assets/samples/image03.jpg','White-Walker'],['assets/samples/image04.jpg','Hulk']]
gr.Interface(inference, [gr.inputs.Image(type="pil"),gr.inputs.Dropdown(choices=['Hulk', 'Zombie', 'White-Walker'], type="value", default='Hulk', label="Model")], gr.outputs.Image(type="file"),title=title,allow_flagging=False,examples=examples,allow_screenshot=False).launch()
|