Spaces:
Running
Running
File size: 4,349 Bytes
a660631 566c28a a660631 84448a9 a660631 84448a9 a660631 566c28a a660631 208f8fb a660631 ae34a8d a660631 ae34a8d a660631 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
#!/usr/bin/env python
import gradio as gr
from settings import DEFAULT_NUM_IMAGES, MAX_IMAGE_RESOLUTION, MAX_NUM_IMAGES
from utils import randomize_seed_fn
def create_demo(process):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
image = gr.Image()
prompt = gr.Textbox(label='Prompt')
run_button = gr.Button('Run')
with gr.Accordion('Advanced options', open=False):
preprocessor_name = gr.Radio(
label='Preprocessor',
choices=['HED', 'PidiNet', 'None'],
type='value',
value='HED')
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=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,
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,
).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,
api_name='scribble',
)
return demo
if __name__ == '__main__':
from model import Model
model = Model(task_name='scribble')
demo = create_demo(model.process_scribble)
demo.queue().launch()
|