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#!/usr/bin/env python
from __future__ import annotations
import pathlib
import gradio as gr
import numpy as np
from model import Model
DESCRIPTION = '# [Self-Distilled StyleGAN](https://github.com/self-distilled-stylegan/self-distilled-internet-photos)'
def get_sample_image_url(name: str) -> str:
sample_image_dir = 'https://huggingface.co/spaces/hysts/Self-Distilled-StyleGAN/resolve/main/samples'
return f'{sample_image_dir}/{name}.jpg'
def get_sample_image_markdown(name: str) -> str:
url = get_sample_image_url(name)
size = name.split('_')[1]
truncation_type = '_'.join(name.split('_')[2:])
return f'''
- size: {size}x{size}
- seed: 0-99
- truncation: 0.7
- truncation type: {truncation_type}
'''
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''
model = Model()
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Tabs():
with gr.TabItem('App'):
with gr.Row():
with gr.Column():
with gr.Group():
model_name = gr.Dropdown(label='Model',
choices=model.MODEL_NAMES,
value=model.MODEL_NAMES[0])
seed = gr.Slider(label='Seed',
minimum=0,
maximum=np.iinfo(np.uint32).max,
step=1,
value=0)
psi = gr.Slider(label='Truncation psi',
minimum=0,
maximum=2,
step=0.05,
value=0.7)
truncation_type = gr.Dropdown(
label='Truncation Type',
choices=model.TRUNCATION_TYPES,
value=model.TRUNCATION_TYPES[0])
run_button = gr.Button('Run')
with gr.Column():
result = gr.Image(label='Result', elem_id='result')
with gr.TabItem('Sample Images'):
with gr.Row():
paths = sorted(pathlib.Path('samples').glob('*'))
names = [path.stem for path in paths]
model_name2 = gr.Dropdown(label='Type',
choices=names,
value='dogs_1024_multimodal_lpips')
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(label='Model',
choices=model.MODEL_NAMES,
value=model.MODEL_NAMES[0])
with gr.Row():
text = get_cluster_center_image_markdown(model_name3.value)
cluster_center_images = gr.Markdown(value=text)
model_name.change(fn=model.set_model, inputs=model_name)
run_button.click(fn=model.set_model_and_generate_image,
inputs=[
model_name,
seed,
psi,
truncation_type,
],
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.queue(max_size=10).launch()
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