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#!/usr/bin/env python
from __future__ import annotations
import argparse
import gradio as gr
from model import Model
TITLE = '# Anime Face Generation with [Diffusers](https://github.com/huggingface/diffusers)'
DESCRIPTION = 'Expected execution time on Hugging Face Spaces: 5s (DDIM, 20 steps), 6s (PNDM, 20 steps), 247s (DDPM, 1000 steps)'
FOOTER = '<img id="visitor-badge" src="https://visitor-badge.glitch.me/badge?page_id=hysts.diffusers-anime-faces" alt="visitor badge" />'
def get_sample_image_url(file_name: str) -> str:
sample_image_dir = 'https://huggingface.co/spaces/hysts/diffusers-anime-faces/resolve/main/samples'
return f'{sample_image_dir}/{file_name}'
def get_sample_image_markdown(name: str) -> str:
model_name = name.split()[0]
if name == 'ddpm-128-exp000 (DDPM)':
scheduler = 'DDPM'
steps = 1000
file_name = f'{model_name}.png'
elif name == 'ddpm-128-exp000 (DDIM, 20 steps)':
scheduler = 'DDIM'
steps = 20
file_name = f'{model_name}-ddim-20steps.png'
else:
raise ValueError
url = get_sample_image_url(file_name)
text = f'''
- size: 128x128
- seed: 0-99
- scheduler: {scheduler}
- steps: {steps}
'''
return text
def update_scheduler_type(name: str) -> dict:
visible = name != 'DDPM'
if name == 'PNDM':
minimum = 4
maximum = 100
else:
minimum = 1
maximum = 200
return gr.Slider.update(visible=visible,
minimum=minimum,
maximum=maximum,
value=20)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
args = parser.parse_args()
model = Model(args.device)
with gr.Blocks(css='style.css') as demo:
gr.Markdown(TITLE)
with gr.Tabs():
with gr.TabItem('Simple Mode'):
run_button_simple = gr.Button('Generate')
result_simple = gr.Image(show_label=False,
elem_id='result-grid')
with gr.TabItem('Advanced Mode'):
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
with gr.Group():
model_name = gr.Dropdown(
model.MODEL_NAMES,
value=model.MODEL_NAMES[0],
label='Model',
interactive=False)
scheduler_type = gr.Radio(
choices=['DDPM', 'DDIM', 'PNDM'],
value='DDIM',
label='Scheduler')
num_steps = gr.Slider(1,
200,
step=1,
value=20,
label='Number of Steps')
seed = gr.Slider(0,
100000,
step=1,
value=1234,
label='Seed')
run_button = gr.Button('Run')
with gr.Column():
result = gr.Image(show_label=False, elem_id='result')
with gr.TabItem('Sample Images'):
with gr.Row():
model_name2 = gr.Dropdown([
'ddpm-128-exp000 (DDPM)',
'ddpm-128-exp000 (DDIM, 20 steps)',
],
value='ddpm-128-exp000 (DDPM)',
label='Model')
with gr.Row():
text = get_sample_image_markdown(model_name2.value)
sample_images = gr.Markdown(text)
gr.Markdown(FOOTER)
model_name.change(fn=model.set_pipeline,
inputs=[
model_name,
scheduler_type,
],
outputs=None)
scheduler_type.change(fn=update_scheduler_type,
inputs=scheduler_type,
outputs=num_steps,
queue=False)
scheduler_type.change(fn=model.set_pipeline,
inputs=[
model_name,
scheduler_type,
],
outputs=None)
run_button_simple.click(fn=model.run_simple,
inputs=None,
outputs=result_simple)
run_button.click(fn=model.run,
inputs=[
model_name,
scheduler_type,
num_steps,
seed,
],
outputs=result)
model_name2.change(fn=get_sample_image_markdown,
inputs=model_name2,
outputs=sample_images)
demo.launch(enable_queue=True, share=False)
if __name__ == '__main__':
main()
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