File size: 2,191 Bytes
28d2a3a
56a97f7
148a19e
c250292
dd53e8c
b1775dd
ceab150
50b7dca
dd8ad63
46bd6e1
810d24d
a9636e0
b767bd0
46bd6e1
d08bf1f
ed3cb4e
dd8ad63
148a19e
dd8ad63
45f8f81
148a19e
72015f8
 
 
 
 
148a19e
56a97f7
f30be72
09a9a44
0c7c6c4
eb55bfe
72015f8
212ad48
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
import os
from PIL import Image
import torch
import gradio as gr



model2 = torch.hub.load(
    "AK391/animegan2-pytorch:main",
    "generator",
    pretrained=True,
    device="cuda",
    progress=False
)


model1 = torch.hub.load("AK391/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v1",  device="cuda")
face2paint = torch.hub.load(
    'AK391/animegan2-pytorch:main', 'face2paint', 
    size=512, device="cuda",side_by_side=False
)
def inference(img, ver):
    if ver == 'version 2 (🔺 robustness,🔻 stylization)':
        out = face2paint(model2, img)
    else:
        out = face2paint(model1, img)
    return out
  
title = "AnimeGANv2"
description = "Gradio Demo for AnimeGanv2 Face Portrait. 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 Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
examples=[['groot.jpeg','version 2 (🔺 robustness,🔻 stylization)'],['bill.png','version 1 (🔺 stylization, 🔻 robustness)'],['tony.png','version 1 (🔺 stylization, 🔻 robustness)'],['elon.png','version 2 (🔺 robustness,🔻 stylization)'],['IU.png','version 1 (🔺 stylization, 🔻 robustness)'],['billie.png','version 2 (🔺 robustness,🔻 stylization)'],['will.png','version 2 (🔺 robustness,🔻 stylization)'],['beyonce.png','version 1 (🔺 stylization, 🔻 robustness)'],['gongyoo.jpeg','version 1 (🔺 stylization, 🔻 robustness)']]
gr.Interface(inference, [gr.inputs.Image(type="pil"),gr.inputs.Radio(['version 1 (🔺 stylization, 🔻 robustness)','version 2 (🔺 robustness,🔻 stylization)'], type="value", default='version 2 (🔺 robustness,🔻 stylization)', label='version')
], gr.outputs.Image(type="pil"),title=title,description=description,article=article,examples=examples,allow_flagging=False,allow_screenshot=False).launch(enable_queue=True,cache_examples=True)