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import spaces |
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import gradio as gr |
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from transformers import pipeline |
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pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") |
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@spaces.GPU |
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def predict(input_img): |
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predictions = pipeline(input_img) |
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return input_img, {p["label"]: p["score"] for p in predictions} |
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_HEADER_ = ''' |
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<h2>Toon3D: Seeing Cartoons from a New Perspective</h2> |
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**Toon3D** lifts cartoons into 3D via aligning and warping backprojected monocular depth predictions.. |
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Project page @ <a href='https://toon3d.studio/' target='_blank'>https://toon3d.studio/</a> |
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**Important Notes:** |
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- Our demo can export a .obj mesh with vertex colors or a .glb mesh now. If you prefer to export a .obj mesh with a **texture map**, please refer to our <a href='https://github.com/TencentARC/InstantMesh?tab=readme-ov-file#running-with-command-line' target='_blank'>Github Repo</a>. |
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- The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42). |
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''' |
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gradio_app = gr.Interface( |
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predict, |
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inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"), |
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outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)], |
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title="Toon3D", |
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) |
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with gr.Blocks() as demo: |
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gr.Markdown(_HEADER_) |
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with gr.Row(variant="panel"): |
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with gr.Column(): |
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with gr.Row(): |
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input = gr.File(file_count="directory") |
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if __name__ == "__main__": |
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gradio_app.launch() |
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