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Running
on
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Running
on
Zero
| #!/usr/bin/env python | |
| 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: | |
| 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, output_image_size=128) | |
| 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.Video(label='Result', elem_id='result-2') | |
| 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) | |
| image_size = gr.Slider(label='Image size', | |
| minimum=64, | |
| maximum=256, | |
| step=64, | |
| value=128) | |
| render_mode = gr.Dropdown(label='Render mode', | |
| choices=['nerf', 'stf'], | |
| value='nerf', | |
| visible=False) | |
| gr.Examples(examples=examples, | |
| inputs=image, | |
| outputs=result, | |
| fn=process_example_fn, | |
| cache_examples=CACHE_EXAMPLES) | |
| inputs = [ | |
| image, | |
| seed, | |
| guidance_scale, | |
| num_inference_steps, | |
| image_size, | |
| render_mode, | |
| ] | |
| run_button.click( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| ).then( | |
| fn=model.run_image, | |
| inputs=inputs, | |
| outputs=result, | |
| ) | |
| return demo | |