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#!/usr/bin/env python
import gradio as gr
import numpy as np
from utils import randomize_seed_fn
def create_canvas(w, h):
return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255
def create_demo(process, max_images=12, default_num_images=3):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
canvas_width = gr.Slider(label='Canvas width',
minimum=256,
maximum=512,
value=512,
step=1)
canvas_height = gr.Slider(label='Canvas height',
minimum=256,
maximum=512,
value=512,
step=1)
create_button = gr.Button('Open drawing canvas!')
image = gr.Image(tool='sketch', brush_radius=10)
prompt = gr.Textbox(label='Prompt')
run_button = gr.Button('Run')
with gr.Accordion('Advanced options', open=False):
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)
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')
create_button.click(fn=create_canvas,
inputs=[canvas_width, canvas_height],
outputs=image,
queue=False)
inputs = [
image,
prompt,
a_prompt,
n_prompt,
num_samples,
image_resolution,
num_steps,
guidance_scale,
seed,
]
prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
).then(
fn=process,
inputs=inputs,
outputs=result,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
).then(
fn=process,
inputs=inputs,
outputs=result,
)
return demo
if __name__ == '__main__':
from model import Model
model = Model(task_name='scribble')
demo = create_demo(model.process_scribble_interactive)
demo.queue().launch()
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