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import shlex
import subprocess
subprocess.run(shlex.split("pip install pip==24.0"), check=True)
subprocess.run(
shlex.split(
"pip install package/onnxruntime_gpu-1.17.0-cp310-cp310-manylinux_2_28_x86_64.whl --force-reinstall --no-deps"
), check=True
)
subprocess.run(
shlex.split(
"pip install package/nvdiffrast-0.3.1.torch-cp310-cp310-linux_x86_64.whl --force-reinstall --no-deps"
), check=True
)
if __name__ == "__main__":
from huggingface_hub import snapshot_download
snapshot_download("public-data/Unique3D", repo_type="model", local_dir="./ckpt")
import os
import sys
sys.path.append(os.curdir)
import torch
torch.set_float32_matmul_precision('medium')
torch.backends.cuda.matmul.allow_tf32 = True
torch.set_grad_enabled(False)
import fire
import gradio as gr
from gradio_app.gradio_3dgen import create_ui as create_3d_ui
from gradio_app.all_models import model_zoo
_TITLE = '''Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image'''
_DESCRIPTION = '''
<div>
<a style="display:inline-block" href='https://github.com/AiuniAI/Unique3D'><img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/AiuniAI/Unique3D?style=social">
</a>
<img alt="GitHub License" src="https://img.shields.io/github/license/AiuniAI/Unique3D">
</div>
# [Paper](https://arxiv.org/abs/2405.20343) | [Project page](https://wukailu.github.io/Unique3D/) | [Huggingface Demo](https://huggingface.co/spaces/Wuvin/Unique3D) | [Gradio Demo](http://unique3d.demo.avar.cn/) | [Online Demo](https://www.aiuni.ai/)
* High-fidelity and diverse textured meshes generated by Unique3D from single-view images.
* The demo is still under construction, and more features are expected to be implemented soon.
* If the Huggingface Demo is overcrowded or fails to produce stable results, you can use the Online Demo [aiuni.ai](https://www.aiuni.ai/), which is free to try (get the registration invitation code Join Discord: https://discord.gg/aiuni). However, the Online Demo is slightly different from the Gradio Demo, in that the inference speed is slower, but the generation is much more stable.
<div style="display: flex; flex-direction: column; justify-content: center; align-items: center; text-align: center; margin-top: 20px;">
<p style="display: flex; gap: 6px;">
<a href="https://huggingface.co/spaces/cavargas10/Unico3D?duplicate=true">
<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg.svg" alt="Duplicate this Space">
</a>
</p>
<p>Click the button above to duplicate this space and enjoy faster inference on the GPU of your choice.</p>
<p style="margin-top: 10px;">Special thanks to <strong>@hysts</strong> for helping to make the "Duplicate this Space" functionality work correctly!</p>
</div>
'''
def launch():
model_zoo.init_models()
with gr.Blocks(
title=_TITLE,
# theme=gr.themes.Monochrome(),
) as demo:
with gr.Row():
with gr.Column(scale=1):
gr.Markdown('# ' + _TITLE)
gr.Markdown(_DESCRIPTION)
create_3d_ui("wkl")
demo.queue().launch(share=True)
if __name__ == '__main__':
fire.Fire(launch) |