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import os
os.system("git clone https://github.com/bryandlee/animegan2-pytorch")
os.system("gdown https://drive.google.com/uc?id=18H3iK09_d54qEDoWIc82SyWB2xun4gjU")
import sys
sys.path.append("animegan2-pytorch")
import torch
torch.set_grad_enabled(False)
from model import Generator
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Generator().eval().to(device)
model.load_state_dict(torch.load("face_paint_512_v2_0.pt"))
from PIL import Image
from torchvision.transforms.functional import to_tensor, to_pil_image
import gradio as gr
def face2paint(
img: Image.Image,
size: int,
side_by_side: bool = False,
) -> Image.Image:
input = to_tensor(img).unsqueeze(0) * 2 - 1
output = model(input.to(device)).cpu()[0]
if side_by_side:
output = torch.cat([input[0], output], dim=2)
output = (output * 0.5 + 0.5).clip(0, 1)
return to_pil_image(output)
import collections
from typing import Union, List
import numpy as np
from PIL import Image
import PIL.Image
import PIL.ImageFile
import numpy as np
import scipy.ndimage
import requests
def inference(img):
out = face2paint(img, 512)
return out
title = "Animeganv2"
description = "Gradio demo for AnimeGanv2 Face Portrait v2. 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</a></p><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'/></p>"
examples=[['groot.jpeg'],['bill.png'],['tony.png'],['elon.png'],['IU.png'],['billie.png'],['will.png'],['beyonce.jpeg'],['gongyoo.jpeg']]
gr.Interface(inference, gr.inputs.Image(type="pil",shape=(512,512)), gr.outputs.Image(type="pil"),title=title,description=description,article=article,examples=examples,enable_queue=True).launch()
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