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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() | |
prompt = gr.Textbox(label="Prompt", submit_btn=True) | |
with gr.Accordion("Advanced options", open=False): | |
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, | |
) | |
canny_low_threshold = gr.Slider( | |
label="Canny low threshold", minimum=1, maximum=255, value=100, step=1 | |
) | |
canny_high_threshold = gr.Slider( | |
label="Canny high threshold", minimum=1, maximum=255, value=200, 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=MAX_SEED, step=1, value=0) | |
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, | |
num_steps, | |
guidance_scale, | |
seed, | |
canny_low_threshold, | |
canny_high_threshold, | |
] | |
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="canny", | |
concurrency_id="main", | |
) | |
return demo | |
if __name__ == "__main__": | |
from model import Model | |
model = Model(task_name="Canny") | |
demo = create_demo(model.process_canny) | |
demo.queue().launch() | |