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  1. app.py +3 -2
app.py CHANGED
@@ -132,8 +132,9 @@ def sample(image, seed, category):
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  import gradio as gr
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  _HEADER_ = '''
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- <h2><b>Official πŸ€— Gradio Demo</b></h2><h2><a href='https://github.com/TencentARC/DI-PCG' target='_blank'><b>DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation</b></a></h2>
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  **DI-PCG** is a diffusion model which directly generates a procedural generator's parameters from a single image, resulting in high-quality 3D meshes.
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  Code: <a href='https://github.com/TencentARC/DI-PCG' target='_blank'>GitHub</a>. Techenical report: <a href='' target='_blank'>ArXiv</a>.
@@ -159,7 +160,7 @@ Please refer to the [LICENSE file]() for details.
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  πŸ“§ **Contact**
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- If you have any questions, feel free to open a discussion or contact us at <b></b>.
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  """
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  def update_examples(category):
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  samples = [[os.path.join(f"examples/{category}", img_name)]
 
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  import gradio as gr
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  _HEADER_ = '''
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+ <h2><b>DI-PCG πŸ€— Gradio Demo</b></h2>
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+ This is official demo for our technical report <a href="">DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation </a>.
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  **DI-PCG** is a diffusion model which directly generates a procedural generator's parameters from a single image, resulting in high-quality 3D meshes.
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  Code: <a href='https://github.com/TencentARC/DI-PCG' target='_blank'>GitHub</a>. Techenical report: <a href='' target='_blank'>ArXiv</a>.
 
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  πŸ“§ **Contact**
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+ If you have any questions, feel free to open a discussion or contact us at <b>[email protected]</b>.
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  """
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  def update_examples(category):
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  samples = [[os.path.join(f"examples/{category}", img_name)]