|
|
|
|
|
import gradio as gr |
|
|
|
|
|
def create_demo(process, max_images=12, default_num_images=3): |
|
with gr.Blocks() as demo: |
|
with gr.Row(): |
|
gr.Markdown('## Control Stable Diffusion with Segmentation Maps') |
|
with gr.Row(): |
|
with gr.Column(): |
|
input_image = gr.Image(source='upload', type='numpy') |
|
prompt = gr.Textbox(label='Prompt') |
|
run_button = gr.Button(label='Run') |
|
with gr.Accordion('Advanced options', open=False): |
|
is_segmentation_map = gr.Checkbox( |
|
label='Is segmentation map', value=False) |
|
num_samples = gr.Slider(label='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) |
|
detect_resolution = gr.Slider( |
|
label='Segmentation Resolution', |
|
minimum=128, |
|
maximum=512, |
|
value=512, |
|
step=1) |
|
num_steps = gr.Slider(label='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=-1, |
|
maximum=2147483647, |
|
step=1, |
|
randomize=True) |
|
a_prompt = gr.Textbox( |
|
label='Added 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, |
|
elem_id='gallery').style(grid=2, |
|
height='auto') |
|
inputs = [ |
|
input_image, |
|
prompt, |
|
a_prompt, |
|
n_prompt, |
|
num_samples, |
|
image_resolution, |
|
detect_resolution, |
|
num_steps, |
|
guidance_scale, |
|
seed, |
|
is_segmentation_map, |
|
] |
|
prompt.submit(fn=process, inputs=inputs, outputs=result) |
|
run_button.click(fn=process, |
|
inputs=inputs, |
|
outputs=result, |
|
api_name='seg') |
|
return demo |
|
|
|
|
|
if __name__ == '__main__': |
|
from model import Model |
|
model = Model() |
|
demo = create_demo(model.process_seg) |
|
demo.queue().launch() |
|
|
