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#!/usr/bin/env python
from __future__ import annotations
import argparse
import os
import pathlib
import subprocess
import gradio as gr
if os.getenv('SYSTEM') == 'spaces':
subprocess.call('pip uninstall -y mmcv-full'.split())
subprocess.call('pip install mmcv-full==1.5.2'.split())
subprocess.call('git apply ../patch'.split(), cwd='Text2Human')
from model import Model
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 set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
def set_example_text(example: list) -> dict:
return gr.Textbox.update(value=example[0])
def main():
args = parse_args()
model = Model(args.device)
css = '''
h1#title {
text-align: center;
}
#input-image {
max-height: 300px;
}
#label-image {
height: 300px;
}
#result-image {
height: 300px;
}
'''
with gr.Blocks(theme=args.theme, css=css) as demo:
gr.Markdown('''<h1 id="title">Text2Human</h1>
This is an unofficial demo for <a href="https://github.com/yumingj/Text2Human">https://github.com/yumingj/Text2Human</a>.
You can modify sample steps and seeds. By varying seeds, you can sample different human images under the same pose, shape description, and texture description. The larger the sample steps, the better quality of the generated images. (The default value of sample steps is 256 in the original repo.)</a>
''')
with gr.Row():
with gr.Column():
with gr.Row():
input_image = gr.Image(label='Input Pose Image',
type='pil',
elem_id='input-image')
with gr.Row():
paths = sorted(pathlib.Path('pose_images').glob('*.png'))
example_images = gr.Dataset(components=[input_image],
samples=[[path.as_posix()]
for path in paths])
with gr.Column():
with gr.Row():
label_image = gr.Image(label='Label Image',
type='numpy',
elem_id='label-image')
with gr.Row():
shape_text = gr.Textbox(
label='Shape Description',
placeholder=
'''<gender>, <sleeve length>, <length of lower clothing>, <outer clothing type>, <other accessories1>, ...
Note: The outer clothing type and accessories can be omitted.''')
with gr.Row():
shape_example_texts = gr.Dataset(
components=[shape_text],
samples=[['man, sleeveless T-shirt, long pants'],
['woman, short-sleeve T-shirt, short jeans']])
with gr.Row():
generate_label_button = gr.Button('Generate Label Image')
with gr.Column():
with gr.Row():
result = gr.Image(label='Result',
type='numpy',
elem_id='result-image')
with gr.Row():
texture_text = gr.Textbox(
label='Texture Description',
placeholder=
'''<upper clothing texture>, <lower clothing texture>, <outer clothing texture>
Note: Currently, only 5 types of textures are supported, i.e., pure color, stripe/spline, plaid/lattice, floral, denim.'''
)
with gr.Row():
texture_example_texts = gr.Dataset(
components=[texture_text],
samples=[['pure color, denim'], ['floral, stripe']])
with gr.Row():
sample_steps = gr.Slider(10,
300,
value=10,
step=10,
label='Sample Steps')
with gr.Row():
seed = gr.Slider(0, 1000000, value=0, step=1, label='Seed')
with gr.Row():
generate_human_button = gr.Button('Generate Human')
gr.Markdown(
'<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.text2human" alt="visitor badge"/></center>'
)
input_image.change(fn=model.process_pose_image,
inputs=[input_image],
outputs=None)
generate_label_button.click(fn=model.generate_label_image,
inputs=[shape_text],
outputs=[label_image])
generate_human_button.click(fn=model.generate_human,
inputs=[
texture_text,
sample_steps,
seed,
],
outputs=[result])
example_images.click(fn=set_example_image,
inputs=example_images,
outputs=example_images.components)
shape_example_texts.click(fn=set_example_text,
inputs=shape_example_texts,
outputs=shape_example_texts.components)
texture_example_texts.click(fn=set_example_text,
inputs=texture_example_texts,
outputs=texture_example_texts.components)
demo.launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
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