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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 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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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.Box(): |
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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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cache_examples=CACHE_EXAMPLES, |
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) |
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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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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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api_name=False, |
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).then( |
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fn=run, |
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inputs=inputs, |
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outputs=result, |
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api_name="image-to-3d", |
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) |
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return demo |
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