app.py

import os
os.system("git clone https://github.com/bryandlee/animegan2-pytorch")

os.system("gdown https://drive.google.com/uc?id=1WK5Mdt6mwlcsqCZMHkCUSDJxN1UyFi0-")
os.system("gdown https://drive.google.com/uc?id=18H3iK09_d54qEDoWIc82SyWB2xun4gjU")
#os.system("pip install dlib")

import sys
sys.path.append("animegan2-pytorch")

import torch
torch.set_grad_enabled(False)

from model import Generator

device = "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:

    w, h = img.size
    s = min(w, h)
    img = img.crop(((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2))
    img = img.resize((size, size), Image.LANCZOS)

    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 os
#import dlib
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(image):
  img = image
  out = face2paint(img, 512)
  return out
  
  

iface = gr.Interface(inference, gr.inputs.Image(type="pil"), gr.outputs.Image(type="pil"))
iface.launch()