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Running
on
Zero
| 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__": | |
| 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. | |
| ''' | |
| 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) | |