Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,715 Bytes
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import gradio as gr
import random
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, 100000000)
return seed
examples = [
[
"condition/example/t2i/bird.jpg",
"A bird made of blue crystal"
],
[
"condition/example/t2i/sofa.png",
"The red sofa in the living room has several pillows on it"
],
[
"condition/example/t2i/house.jpg",
"A brick house with a chimney under a starry sky.",
]
]
def create_demo(process):
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
image = gr.Image()
prompt = gr.Textbox(label="Text prompt")
run_button = gr.Button("Run")
with gr.Accordion("Advanced options", open=False):
preprocessor_name = gr.Radio(
label="Preprocessor",
choices=[
"depth",
"No preprocess",
],
type="value",
value="depth",
info='depth.',
)
cfg_scale = gr.Slider(label="Guidance scale",
minimum=0.1,
maximum=30.0,
value=4,
step=0.1)
control_strength = gr.Slider(minimum=0., maximum=1.0, step=0.1, value=1.0, label="control_strength")
# resolution = gr.Slider(label="(H, W)",
# minimum=384,
# maximum=768,
# value=512,
# step=16)
top_k = gr.Slider(minimum=1,
maximum=16384,
step=1,
value=2000,
label='Top-K')
top_p = gr.Slider(minimum=0.,
maximum=1.0,
step=0.1,
value=1.0,
label="Top-P")
temperature = gr.Slider(minimum=0.,
maximum=1.0,
step=0.1,
value=1.0,
label='Temperature')
seed = gr.Slider(label="Seed",
minimum=0,
maximum=100000000,
step=1,
value=0)
randomize_seed = gr.Checkbox(label="Randomize seed",
value=True)
with gr.Column():
result = gr.Gallery(label="Output",
show_label=False,
height='800px',
columns=2,
object_fit="scale-down")
gr.Examples(
examples=examples,
inputs=[
image,
prompt,
# resolution,
]
)
inputs = [
image,
prompt,
cfg_scale,
temperature,
top_k,
top_p,
seed,
control_strength,
preprocessor_name
]
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=False,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=process,
inputs=inputs,
outputs=result,
api_name="depth",
)
return demo
if __name__ == "__main__":
from model import Model
model = Model()
demo = create_demo(model.process_depth)
demo.queue().launch(share=False, server_name="0.0.0.0")
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