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import os |
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os.system("git clone https://github.com/bryandlee/animegan2-pytorch") |
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os.system("gdown https://drive.google.com/uc?id=1WK5Mdt6mwlcsqCZMHkCUSDJxN1UyFi0-") |
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os.system("gdown https://drive.google.com/uc?id=18H3iK09_d54qEDoWIc82SyWB2xun4gjU") |
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import sys |
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sys.path.append("animegan2-pytorch") |
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import torch |
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torch.set_grad_enabled(False) |
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from model import Generator |
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device = "cpu" |
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model = Generator().eval().to(device) |
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model.load_state_dict(torch.load("face_paint_512_v2_0.pt")) |
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from PIL import Image |
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from torchvision.transforms.functional import to_tensor, to_pil_image |
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import gradio as gr |
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def face2paint( |
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img: Image.Image, |
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size: int, |
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side_by_side: bool = True, |
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) -> Image.Image: |
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w, h = img.size |
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s = min(w, h) |
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img = img.crop(((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2)) |
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img = img.resize((size, size), Image.LANCZOS) |
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input = to_tensor(img).unsqueeze(0) * 2 - 1 |
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output = model(input.to(device)).cpu()[0] |
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if side_by_side: |
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output = torch.cat([input[0], output], dim=2) |
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output = (output * 0.5 + 0.5).clip(0, 1) |
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return to_pil_image(output) |
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import os |
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import collections |
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from typing import Union, List |
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import numpy as np |
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from PIL import Image |
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import PIL.Image |
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import PIL.ImageFile |
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import numpy as np |
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import scipy.ndimage |
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import requests |
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def inference(image): |
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img = image |
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out = face2paint(img, 512) |
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return out |
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iface = gr.Interface(inference, gr.inputs.Image(type="pil"), gr.outputs.Image(type="pil")) |
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iface.launch() |
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