| |
|
|
| 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='filepath') |
| 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 |
|
|
