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#!/usr/bin/env python
import gradio as gr
from utils import randomize_seed_fn
def create_demo(process, max_images=12, default_num_images=3):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
image = gr.Image()
prompt = gr.Textbox(label='Prompt')
run_button = gr.Button(label='Run')
with gr.Accordion('Advanced options', open=False):
preprocessor_name = gr.Radio(label='Preprocessor',
choices=['Openpose', 'None'],
type='value',
value='Openpose')
num_samples = gr.Slider(label='Number of images',
minimum=1,
maximum=max_images,
value=default_num_images,
step=1)
image_resolution = gr.Slider(label='Image resolution',
minimum=256,
maximum=512,
value=512,
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=9.0,
step=0.1)
seed = gr.Slider(label='Seed',
minimum=0,
maximum=1000000,
step=1,
value=0,
randomize=True)
randomize_seed = gr.Checkbox(label='Randomize seed',
value=True)
a_prompt = gr.Textbox(
label='Additional prompt',
value='best quality, extremely detailed')
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).style(
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,
).then(
fn=process,
inputs=inputs,
outputs=result,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
).then(
fn=process,
inputs=inputs,
outputs=result,
api_name='openpose',
)
return demo
if __name__ == '__main__':
from model import Model
model = Model(task_name='Openpose')
demo = create_demo(model.process_openpose)
demo.queue().launch()
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