import shlex import subprocess subprocess.run( shlex.split( "pip install package/onnxruntime_gpu-1.17.0-cp310-cp310-manylinux_2_28_x86_64.whl --force-reinstall --no-deps" ) ) subprocess.run( shlex.split( "pip install package/nvdiffrast-0.3.1.torch-cp310-cp310-linux_x86_64.whl --force-reinstall --no-deps" ) ) if __name__ == "__main__": import os from huggingface_hub import login hf_token = os.environ.get("HF_TOKEN") login(token=hf_token) 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 = '''
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# [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)