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from __future__ import annotations |
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import argparse |
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import gradio as gr |
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import numpy as np |
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from model import Model |
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TITLE = '# Self-Distilled StyleGAN' |
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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). |
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Expected execution time on Hugging Face Spaces: 2s''' |
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FOOTER = '<img id="visitor-badge" src="https://visitor-badge.glitch.me/badge?page_id=hysts.self-distilled-stylegan" alt="visitor badge" />' |
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def parse_args() -> argparse.Namespace: |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--device', type=str, default='cpu') |
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parser.add_argument('--theme', type=str) |
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parser.add_argument('--share', action='store_true') |
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parser.add_argument('--port', type=int) |
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parser.add_argument('--disable-queue', |
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dest='enable_queue', |
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action='store_false') |
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return parser.parse_args() |
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def get_sample_image_url(model_name: str) -> str: |
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sample_image_dir = 'https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/samples' |
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return f'{sample_image_dir}/{model_name}.jpg' |
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def get_sample_image_markdown(model_name: str) -> str: |
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url = get_sample_image_url(model_name) |
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size = model_name.split('_')[-1] |
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return f''' |
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- size: {size}x{size} |
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- seed: 0-99 |
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- truncation: 0.7 |
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''' |
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def get_cluster_center_image_url(model_name: str) -> str: |
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cluster_center_image_dir = 'https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/cluster_center_images' |
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return f'{cluster_center_image_dir}/{model_name}.jpg' |
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def get_cluster_center_image_markdown(model_name: str) -> str: |
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url = get_cluster_center_image_url(model_name) |
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return f'' |
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def main(): |
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args = parse_args() |
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model = Model(args.device) |
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with gr.Blocks(theme=args.theme, css='style.css') as demo: |
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gr.Markdown(TITLE) |
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gr.Markdown(DESCRIPTION) |
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with gr.Tabs(): |
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with gr.TabItem('App'): |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Group(): |
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model_name = gr.Dropdown( |
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model.MODEL_NAMES, |
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value=model.MODEL_NAMES[0], |
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label='Model') |
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seed = gr.Slider(0, |
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np.iinfo(np.uint32).max, |
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value=0, |
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step=1, |
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label='Seed') |
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psi = gr.Slider(0, |
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2, |
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step=0.05, |
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value=0.7, |
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label='Truncation psi') |
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multimodal_truncation = gr.Checkbox( |
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label='Multi-modal Truncation', value=True) |
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run_button = gr.Button('Run') |
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with gr.Column(): |
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result = gr.Image(label='Result', elem_id='result') |
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with gr.TabItem('Sample Images'): |
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with gr.Row(): |
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model_name2 = gr.Dropdown(model.MODEL_NAMES, |
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value=model.MODEL_NAMES[0], |
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label='Model') |
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with gr.Row(): |
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text = get_sample_image_markdown(model_name2.value) |
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sample_images = gr.Markdown(text) |
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with gr.TabItem('Cluster Center Images'): |
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with gr.Row(): |
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model_name3 = gr.Dropdown(model.MODEL_NAMES, |
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value=model.MODEL_NAMES[0], |
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label='Model') |
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with gr.Row(): |
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text = get_cluster_center_image_markdown(model_name3.value) |
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cluster_center_images = gr.Markdown(value=text) |
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gr.Markdown(FOOTER) |
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model_name.change(fn=model.set_model, inputs=model_name, outputs=None) |
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run_button.click(fn=model.set_model_and_generate_image, |
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inputs=[ |
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model_name, |
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seed, |
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psi, |
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multimodal_truncation, |
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], |
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outputs=result) |
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model_name2.change(fn=get_sample_image_markdown, |
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inputs=model_name2, |
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outputs=sample_images) |
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model_name3.change(fn=get_cluster_center_image_markdown, |
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inputs=model_name3, |
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outputs=cluster_center_images) |
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demo.launch( |
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enable_queue=args.enable_queue, |
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server_port=args.port, |
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share=args.share, |
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) |
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if __name__ == '__main__': |
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main() |
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