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#!/usr/bin/env python
from __future__ import annotations
import os
import gradio as gr
from model import Model
DESCRIPTION = '''# [TEXTure](https://github.com/TEXTurePaper/TEXTurePaper)
- This demo only accepts as input `.obj` files with less than 100,000 faces.
- Inference takes about 10 minutes on a T4 GPU.
'''
if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
model = Model()
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
input_shape = gr.Model3D(label='Input 3D mesh')
text = gr.Text(label='Text')
seed = gr.Slider(label='Seed',
minimum=0,
maximum=100000,
value=3,
step=1)
guidance_scale = gr.Slider(label='Guidance scale',
minimum=0,
maximum=50,
value=7.5,
step=0.1)
run_button = gr.Button('Run')
with gr.Column():
progress_text = gr.Text(label='Progress')
with gr.Tabs():
with gr.TabItem(label='Images from each viewpoint'):
viewpoint_images = gr.Gallery(show_label=False).style(
columns=4, height='auto')
with gr.TabItem(label='Result 3D model'):
result_3d_model = gr.Model3D(show_label=False)
with gr.TabItem(label='Output mesh file'):
output_file = gr.File(show_label=False)
with gr.Row():
examples = [
['shapes/dragon1.obj', 'a photo of a dragon', 0, 7.5],
['shapes/dragon2.obj', 'a photo of a dragon', 0, 7.5],
['shapes/eagle.obj', 'a photo of an eagle', 0, 7.5],
['shapes/napoleon.obj', 'a photo of Napoleon Bonaparte', 3, 7.5],
['shapes/nascar.obj', 'A next gen nascar', 2, 10],
]
gr.Examples(examples=examples,
inputs=[
input_shape,
text,
seed,
guidance_scale,
],
outputs=[
result_3d_model,
output_file,
],
cache_examples=False)
run_button.click(fn=model.run,
inputs=[
input_shape,
text,
seed,
guidance_scale,
],
outputs=[
viewpoint_images,
result_3d_model,
output_file,
progress_text,
])
demo.queue(max_size=5).launch(debug=True)
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