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#!/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
'''
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''
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()
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