File size: 5,156 Bytes
45bcca5
 
 
 
 
 
 
 
fed7f36
 
 
 
 
 
 
 
45bcca5
 
 
 
 
 
 
 
 
 
 
 
 
 
fed7f36
 
 
45bcca5
 
fed7f36
 
 
 
 
 
 
 
45bcca5
 
fed7f36
 
 
45bcca5
 
fed7f36
 
 
45bcca5
 
 
 
fed7f36
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
45bcca5
 
 
 
 
 
 
 
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
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
#!/usr/bin/env python

from __future__ import annotations

import argparse

import gradio as gr
import numpy as np

from model import Model

TITLE = '# Self-Distilled StyleGAN'
DESCRIPTION = '''This is an unofficial demo for [https://github.com/self-distilled-stylegan/self-distilled-internet-photos](https://github.com/self-distilled-stylegan/self-distilled-internet-photos).

Expected execution time on Hugging Face Spaces: 2s'''
FOOTER = '<img id="visitor-badge" src="https://visitor-badge.glitch.me/badge?page_id=hysts.self-distilled-stylegan" alt="visitor badge" />'


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--device', type=str, default='cpu')
    parser.add_argument('--theme', type=str)
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--port', type=int)
    parser.add_argument('--disable-queue',
                        dest='enable_queue',
                        action='store_false')
    return parser.parse_args()


def get_sample_image_url(model_name: str) -> str:
    sample_image_dir = 'https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/samples'
    return f'{sample_image_dir}/{model_name}.jpg'


def get_sample_image_markdown(model_name: str) -> str:
    url = get_sample_image_url(model_name)
    size = model_name.split('_')[-1]
    return f'''
    - size: {size}x{size}
    - seed: 0-99
    - truncation: 0.7
    ![sample images]({url})'''


def get_cluster_center_image_url(model_name: str) -> str:
    cluster_center_image_dir = 'https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/cluster_center_images'
    return f'{cluster_center_image_dir}/{model_name}.jpg'


def get_cluster_center_image_markdown(model_name: str) -> str:
    url = get_cluster_center_image_url(model_name)
    return f'![cluster center images]({url})'


def main():
    args = parse_args()

    model = Model(args.device)

    with gr.Blocks(theme=args.theme, css='style.css') as demo:
        gr.Markdown(TITLE)
        gr.Markdown(DESCRIPTION)

        with gr.Tabs():
            with gr.TabItem('App'):
                with gr.Row():
                    with gr.Column():
                        with gr.Group():
                            model_name = gr.Dropdown(
                                model.MODEL_NAMES,
                                value=model.MODEL_NAMES[0],
                                label='Model')
                            seed = gr.Slider(0,
                                             np.iinfo(np.uint32).max,
                                             value=0,
                                             step=1,
                                             label='Seed')
                            psi = gr.Slider(0,
                                            2,
                                            step=0.05,
                                            value=0.7,
                                            label='Truncation psi')
                            multimodal_truncation = gr.Checkbox(
                                label='Multi-modal Truncation', value=True)
                            run_button = gr.Button('Run')
                    with gr.Column():
                        result = gr.Image(label='Result', elem_id='result')
            with gr.TabItem('Sample Images'):
                with gr.Row():
                    model_name2 = gr.Dropdown(model.MODEL_NAMES,
                                              value=model.MODEL_NAMES[0],
                                              label='Model')
                with gr.Row():
                    text = get_sample_image_markdown(model_name2.value)
                    sample_images = gr.Markdown(text)
            with gr.TabItem('Cluster Center Images'):
                with gr.Row():
                    model_name3 = gr.Dropdown(model.MODEL_NAMES,
                                              value=model.MODEL_NAMES[0],
                                              label='Model')
                with gr.Row():
                    text = get_cluster_center_image_markdown(model_name3.value)
                    cluster_center_images = gr.Markdown(value=text)

        gr.Markdown(FOOTER)

        model_name.change(fn=model.set_model, inputs=model_name, outputs=None)
        run_button.click(fn=model.set_model_and_generate_image,
                         inputs=[
                             model_name,
                             seed,
                             psi,
                             multimodal_truncation,
                         ],
                         outputs=result)
        model_name2.change(fn=get_sample_image_markdown,
                           inputs=model_name2,
                           outputs=sample_images)
        model_name3.change(fn=get_cluster_center_image_markdown,
                           inputs=model_name3,
                           outputs=cluster_center_images)

    demo.launch(
        enable_queue=args.enable_queue,
        server_port=args.port,
        share=args.share,
    )


if __name__ == '__main__':
    main()