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import shlex |
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import subprocess |
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import gradio as gr |
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from model import Model |
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from settings import CACHE_EXAMPLES, MAX_SEED |
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from utils import randomize_seed_fn |
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def create_demo(model: Model) -> gr.Blocks: |
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subprocess.run( |
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shlex.split( |
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'wget https://raw.githubusercontent.com/openai/shap-e/d99cedaea18e0989e340163dbaeb4b109fa9e8ec/shap_e/examples/example_data/corgi.png -O corgi.png' |
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)) |
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examples = ['corgi.png'] |
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def process_example_fn(image_path: str) -> str: |
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return model.run_image(image_path, output_image_size=128) |
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with gr.Blocks() as demo: |
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with gr.Box(): |
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image = gr.Image(label='Input image', |
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show_label=False, |
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type='filepath') |
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run_button = gr.Button('Run') |
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result = gr.Video(label='Result', elem_id='result-2') |
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with gr.Accordion('Advanced options', open=False): |
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seed = gr.Slider(label='Seed', |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0) |
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randomize_seed = gr.Checkbox(label='Randomize seed', |
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value=True) |
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guidance_scale = gr.Slider(label='Guidance scale', |
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minimum=1, |
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maximum=20, |
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step=0.1, |
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value=3.0) |
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num_inference_steps = gr.Slider( |
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label='Number of inference steps', |
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minimum=1, |
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maximum=100, |
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step=1, |
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value=64) |
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image_size = gr.Slider(label='Image size', |
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minimum=64, |
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maximum=256, |
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step=64, |
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value=128) |
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render_mode = gr.Dropdown(label='Render mode', |
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choices=['nerf', 'stf'], |
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value='nerf', |
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visible=False) |
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gr.Examples(examples=examples, |
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inputs=image, |
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outputs=result, |
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fn=process_example_fn, |
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cache_examples=CACHE_EXAMPLES) |
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inputs = [ |
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image, |
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seed, |
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guidance_scale, |
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num_inference_steps, |
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image_size, |
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render_mode, |
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] |
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run_button.click( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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).then( |
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fn=model.run_image, |
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inputs=inputs, |
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outputs=result, |
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) |
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return demo |
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