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#!/usr/bin/env python
import gradio as gr
from settings import (
DEFAULT_IMAGE_RESOLUTION,
DEFAULT_NUM_IMAGES,
MAX_IMAGE_RESOLUTION,
MAX_NUM_IMAGES,
MAX_SEED,
)
from utils import randomize_seed_fn
def create_demo(process):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
image = gr.Image(value='images/seg_demo.png')
prompt = gr.Textbox(label="Prompt", value='A large building with a pointed roof and several chimneys')
run_button = gr.Button("Run")
with gr.Accordion("Advanced options", open=False):
preprocessor_name = gr.Radio(
label="Preprocessor", choices=["UPerNet", "None"], type="value", value="None"
)
num_samples = gr.Slider(
label="Number of images", minimum=1, maximum=MAX_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, step=1
)
image_resolution = gr.Slider(
label="Image resolution",
minimum=256,
maximum=MAX_IMAGE_RESOLUTION,
value=DEFAULT_IMAGE_RESOLUTION,
step=256,
)
preprocess_resolution = gr.Slider(
label="Preprocess resolution", minimum=128, maximum=512, value=512, step=1
)
num_steps = gr.Slider(label="Number of steps", minimum=1, maximum=100, value=20, step=1)
guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
n_prompt = gr.Textbox(
label="Negative prompt",
value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
)
with gr.Column():
result = gr.Gallery(label="Output", show_label=False, columns=2, object_fit="scale-down")
inputs = [
image,
prompt,
a_prompt,
n_prompt,
num_samples,
image_resolution,
preprocess_resolution,
num_steps,
guidance_scale,
seed,
preprocessor_name,
]
prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=process,
inputs=inputs,
outputs=result,
api_name=False,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=process,
inputs=inputs,
outputs=result,
api_name="segmentation",
)
return demo
if __name__ == "__main__":
from model import Model
model = Model(task_name="segmentation")
demo = create_demo(model.process_segmentation)
demo.queue().launch()
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