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#!/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,
api_name=False,
).then(
fn=model.run_image,
inputs=inputs,
outputs=result,
api_name="image-to-3d",
)
return demo
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