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on
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Running
on
T4
| 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") | |
| 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 os | |
| 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." | |
| article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo</a></p>" | |
| examples=[['groot.jpeg'],['bill.png'],['tony.png'],['elon.png'],['iu.png'],['billy.png'],['bob.png']] | |
| 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() | |