arabago96 commited on
Commit
4ee6d61
·
1 Parent(s): 10392b8

CRITICAL FIX: Replace deprecated gr.Row.update() with gr.update() for Gradio 5.x compatibility + ASM branding

Browse files
Files changed (1) hide show
  1. app.py +4 -4
app.py CHANGED
@@ -281,11 +281,11 @@ def split_image(image: Image.Image) -> List[Image.Image]:
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  with gr.Blocks(delete_cache=(600, 600)) as demo:
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  gr.Markdown("""
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- ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
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  * Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
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  * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
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- New: 1) Experimental multi-image support. 2) Gaussian file extraction.
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  """)
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  with gr.Row():
@@ -366,11 +366,11 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
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  demo.unload(end_session)
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  single_image_input_tab.select(
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- lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
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  outputs=[is_multiimage, single_image_example, multiimage_example]
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  )
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  multiimage_input_tab.select(
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- lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]),
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  outputs=[is_multiimage, single_image_example, multiimage_example]
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  )
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  with gr.Blocks(delete_cache=(600, 600)) as demo:
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  gr.Markdown("""
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+ ## ASM - Advanced Spatial Modeling for 3D Generation
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  * Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
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  * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
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+ Features: 1) Multi-image support. 2) Gaussian file extraction. 3) Advanced 3D generation.
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  """)
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  with gr.Row():
 
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  demo.unload(end_session)
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  single_image_input_tab.select(
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+ lambda: tuple([False, gr.update(visible=True), gr.update(visible=False)]),
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  outputs=[is_multiimage, single_image_example, multiimage_example]
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  )
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  multiimage_input_tab.select(
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+ lambda: tuple([True, gr.update(visible=False), gr.update(visible=True)]),
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  outputs=[is_multiimage, single_image_example, multiimage_example]
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  )
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