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import pathlib
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import shlex
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import subprocess
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import gradio as gr
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import PIL.Image
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import spaces
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from model import Model
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from settings import 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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if not pathlib.Path("corgi.png").exists():
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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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)
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examples = ["corgi.png"]
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@spaces.GPU
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def process_example_fn(image_path: str) -> str:
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return model.run_image(image_path)
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@spaces.GPU
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def run(image: PIL.Image.Image, seed: int, guidance_scale: float, num_inference_steps: int) -> str:
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return model.run_image(image, seed, guidance_scale, num_inference_steps)
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with gr.Blocks() as demo:
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with gr.Group():
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image = gr.Image(label="Input image", show_label=False, type="pil")
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run_button = gr.Button("Run")
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result = gr.Model3D(label="Result", show_label=False)
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with gr.Accordion("Advanced options", open=False):
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seed = gr.Slider(
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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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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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guidance_scale = gr.Slider(
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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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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=2,
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maximum=100,
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step=1,
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value=64,
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)
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gr.Examples(
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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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)
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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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api_name=False,
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concurrency_limit=None,
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).then(
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fn=run,
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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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],
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outputs=result,
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api_name="image-to-3d",
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concurrency_id="gpu",
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concurrency_limit=1,
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)
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return demo
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