app_image_to_3d.py

#!/usr/bin/env python

import pathlib
import shlex
import subprocess

import gradio as gr

from model import Model
from settings import CACHE_EXAMPLES, MAX_SEED
from utils import randomize_seed_fn


def create_demo(model: Model) -> gr.Blocks:
    if not pathlib.Path('corgi.png').exists():
        subprocess.run(
            shlex.split(
                'wget https://raw.githubusercontent.com/openai/shap-e/d99cedaea18e0989e340163dbaeb4b109fa9e8ec/shap_e/examples/example_data/corgi.png -O corgi.png'
            ))
    examples = ['corgi.png']

    def process_example_fn(image_path: str) -> str:
        return model.run_image(image_path)

    with gr.Blocks() as demo:
        with gr.Box():
            image = gr.Image(label='Input image', show_label=False, type='pil')
            run_button = gr.Button('Run')
            result = gr.Model3D(label='Result', show_label=False)
            with gr.Accordion('Advanced options', open=False):
                seed = gr.Slider(label='Seed',
                                 minimum=0,
                                 maximum=MAX_SEED,
                                 step=1,
                                 value=0)
                randomize_seed = gr.Checkbox(label='Randomize seed',
                                             value=True)
                guidance_scale = gr.Slider(label='Guidance scale',
                                           minimum=1,
                                           maximum=20,
                                           step=0.1,
                                           value=3.0)
                num_inference_steps = gr.Slider(
                    label='Number of inference steps',
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=64)

        gr.Examples(examples=examples,
                    inputs=image,
                    outputs=result,
                    fn=process_example_fn,
                    cache_examples=CACHE_EXAMPLES)

        inputs = [
            image,
            seed,
            guidance_scale,
            num_inference_steps,
        ]

        run_button.click(
            fn=randomize_seed_fn,
            inputs=[seed, randomize_seed],
            outputs=seed,
            queue=False,
        ).then(
            fn=model.run_image,
            inputs=inputs,
            outputs=result,
            api_name='image-to-3d',
        )
    return demo